authors were also able to retrieve the key for an Android Bitcoin wallet.
Genkin et al. [102] published simultaneously with their work in which they
successfully full key retrieval against OpenSSL’s ECDSA implementation for iOS
and Android. Compared to Belgarric et al. [198], the approach was less
intrusive and simply needed a probe to be put close to the target device;
there were no hardware or software triggers. 5000 signature traces from the
Sony-Ericsson Xperia X10 and the iPhone 3GS were used in the exploit, of which
two (0.04

In 2017, the study on the EM characteristics of program execution in the ARM
TrustZone was done by Bukasa et al. [199]. On a Raspberry Pi 2 with a Broadcom
BCM2836 SoC (quad-core ARM Cortex-A7 at 900MHz), they examined an unencrypted
reference AES software implementation and a dummy PIN verification method. The
impact of execution in a secure world against a non-secure world and multicore
versus single core was investigated. Targeting the first-round S-box of AES
and gathering 150,000 EM traces might be performed to recover the key through
template analysis. Depending on the setup of the system, a success rate of
17.81%–38.30% might be achieved in recovering the key. The success rate for
multicore execution in the secure world was the lowest (17.81%), whereas the
success rate for single-core execution in the non-secure world with the MMU
turned off was the highest (38.30%).

The first physical SCA on an Apple iPhone 4 was recently demonstrated by [49]
for retrieving the 256-bit hardware-based user identifier (UID) key. The UID
is combined with the user’s passcode to produce a passcode key from a
password-based KDF that unwraps keys from the system key bag, which holds per-
file data encryption keys for FBE utilizing the SoC’s AES encryption engine.
An attack in two parts was carried out: the AES algorithm was attacked using
EM correlation power analysis following — 1) utilizing a known bootloader
vulnerability to add an unauthorized component and 2) using EM correlation
power analysis. A trigger from a GPIO pin that the unauthorized bootloader had
modified was used to activate the measurement of EM traces from a probe on the
Apple A4 SoC. A LeCroy WaveRunner 8254M oscilloscope (2.5GHz bandwidth, 40GS/s
sampling rate) and a Langer EMV-Technik RF-B 0,3-3 EM probe, as well as a
Langer EMV-Technik PA 303 SMA amplifier, were employed for the specific
equipment. After 300 million traces were acquired over two weeks, it took up
to three hours of analysis utilizing two Nvidia RTX 2080 TI GPUs before the
key could be recovered.

In another study, Hu, Xiao-yang et al. [200] aimed to develop a new signal
acquisition system for detecting EM side-channel leakage signals from mobile
devices that use the AES encryption algorithm. They developed a technique for
processing the raw side-channel leakage signal and used time-frequency
analysis and filter technology to recover the encryption characteristics of
AES. After carrying out a short-time Fourier Transform (STFT), the authors
observed that the frequency range of the AES encryption traces is 50 MHz to 80
MHz and the whole ten-round encryption features are clearly visible after the
second filter. This study significantly contributes to the field as it
demonstrates a method for locating mobile devices’ side-channel EM leakage
signals.

###  V-E Internet of Things

Since the invention of computers, Moore’s law has allowed engineers to advance
their capabilities continuously. The development of smaller, more energy-
efficient computing equipment, i.e., mobile devices, which have less
processing power but can operate for longer on battery power, has been made
possible by this trend in addition to making computers more prevalent and
resource-rich. The end outcome of these developments is the rise of the IoT,
which consists of small computational devices with sensors and network
connectivity that have already begun to permeate several aspects of daily
life, such as smart home systems, sports, and healthcare [201, 202, 203, 20].
IoT device emissions can originate from a variety of components, with the
processor playing the largest role. The software activity running on the
device has a direct impact on changes in the processor’s EM emissions [204].

Recently, EM-SCA was put forth as a technique for gathering information from
IoT devices that is relevant for forensic purposes [131, 205, 30]. Law
enforcement may be capable of performing EM-SCA procedures on an IoT device as
soon as it is seized if it is powered on. It has been claimed that various
information, such as modified firmware, software behavior, and cryptographic
techniques, can be identified with EM-SCA-based methodologies [4]. There is no
need to physically interact with the equipment under investigation because
collecting and analyzing EM emission signals is non-invasive.

According to Camurati et al. [181] and Sayakkara et al. [4], the use of EM-SCA
to detect cryptographic activities in IoT devices has gained significant
attention in the research community. Camurati et al. [181] discovered that
mixed-signal computers, like system-on-chips (SoCs), which combine a CPU and a
radio transceiver on a single silicon die, can result in long-distance EM
leakages known as ”screaming channels”. This has greatly expanded the possible
attack surface as SoC adoption on IoT devices grows in popularity. On the
other hand, Sayakkara et al. [4] explored the use of EM-SCA to detect
cryptographic activity in IoT devices by focusing on the EM emissions produced
by a Raspberry Pi. The study aimed to automatically detect the data encryption
operations performed by the device using AES-128, AES-256, and 3DES
cryptographic algorithms. The results showed that a neural network classifier
could discriminate between these encryption techniques with 80% accuracy,
demonstrating the potential of EM-SCA as a tool for detecting cryptographic
operations and suggesting its application to detect encryption algorithms on
less capable hardware devices. These findings contribute to the growing body
of literature on the use of EM-SCA in the context of IoT security.

Additionally, several case studies have investigated the vulnerability of IoT
devices to SCA, particularly through the use of EM analysis. Levina et al.
[206] presents a case study that investigates how side-channel attacks can
target pet wearables. This study’s authors concentrated on a Jagger & Lewis
dog activity tracker [207] that was no longer produced. The authors conducted
an EM attack on the device to demonstrate the SCA. They were able to retrieve
the original JSON text by analyzing traces taken at the time of Base64
encoding. In order to analyze the form visually and estimate its amplitude and
temporal properties, they recorded data from the targeted device. According to
the results of the case study, the authors make the assumption that the device
employs Base64, the most straightforward data encoding scheme, to ensure that
the fewest operations are required to account for the tracker’s low power.

Meanwhile, Dinu and Kizhvatov [208] conducted a case study to assess the
vulnerability of Thread, a networking stack developed for secure communication
between IoT devices, to side-channel attacks. They identified several attack
vectors to bypass the security mechanisms of the networking stack, focusing on
manipulating security material such as cryptographic keys. Their fully
implemented attack employed network-level mechanisms and electromagnetic side-
channel analysis techniques to gain unauthorized access to an existing Thread
network. Although Thread has complex security mechanisms, the study found that
such attacks are still feasible and pose a relatively higher threat to the
commercial setting. The full attack was unsuccessful due to a side effect of a
feature not related to security, namely packet fragmentation.

Furthermore, Durvaux and Durvaux [209] explores how SCA against IoT devices
can be carried out using relatively inexpensive components. The author
demonstrates how to construct a full side-channel test bench using an EM probe
and a Red Pitaya STEMlab platform for sampling to measure power leakages. The
authors implement the AES-256 algorithm in C programming language using a
simplistic 8-bit architecture, which is carried out using the Arduino Uno
board’s integrated 8-bit Atmel ATmega328P microcontroller in one use case, and
the 32-bit microcontroller Atmel ATSAM3X8E in the Arduino Due board in
another. The authors demonstrate that pre-processing techniques can increase
the success rate of these attacks. They also outline how to carry out a
comprehensive key recovery attack, focusing on specific key bytes from each
round. They address the use of key enumeration techniques to make up for the
lack of information that can be used in the attack, yet they point out that
this strategy is more difficult for AES-256 due to the dependence between
targeted rounds. The results of their study demonstrate that even with
relatively inexpensive components, SCA can still be carried out effectively on
IoT devices.

##  VI Countermeasures

Figure 10: Variety of EM-SCA countermeasures identified as part of the
literature review

While the primary focus of this literature review is not on countermeasures to
EM-SCA, it would be remiss to omit a brief discussion on the topic. As a
result, this section provides a brief overview of EM-SCA countermeasures in
the literature. Due to the serious implications of EM-SCA in retrieving data
from computing systems, numerous software and hardware countermeasures have
been developed [159, 210, 211, 212, 213, 56]. Nassar et al. [214] proposed a
masking scheme for AES called Rotating S-boxes Masking (RSM). The RSM is used
to protect the AES design against SCAs (DPA, CPA). Generally, the masking
techniques used to protect block cyphers against SCA are based on the internal
modification of the block cypher architectures [214].

Figure 10 illustrates the various countermeasures identified in the literature
review, and their relation to the different categories of EM-SCA attacks.
While these countermeasures can help to mitigate EM-SCA, it is important to
note that they may not be insurmountable, and determined attackers with
sufficient resources and expertise may still be able to circumvent them.

In order to resist primary leaks and make SCA more challenging, one strategy
is hiding and masking [181, 215, 216]. Camurati et al. [181] demonstrated that
the physical separation of analogue and digital components or the use of a
separate processor for cryptographic operations are two alternative methods
for preventing information from leaking into radio signals. The paper also
emphasizes the significance of countermeasure incorporation into chip design,
such as the adoption of digital radio technologies that are less vulnerable to
side-channel attacks and/or the integration of several dies inside a System in
Package (SiP) technology. The research notes that these countermeasures might
have a negative effect on performance and might be challenging to put into
practice without jeopardizing other functional requirements, cost, or chip
size.

In a separate study, Khan et al. [217] aimed to investigate the effectiveness
of shielding techniques in reducing EM emanations from local power grids. He
focused on the use of metal-insulator-metal (MIM) capacitors and the effect of
upper metal layers on EM emanations from lower metal layers. Sheet metal is
commonly used for EM shielding due to its ability to absorb radio and magnetic
waves, however, the authors pointed out that gaps in the shield and electrical
resistivity of the conductor can reduce its shielding capability. The MIM
capacitor, which is composed of two metal plates and a dielectric layer, was
tested as a shielding solution. The results showed that while the MIM
shielding reduced EM emission by almost 3 dB, it may also increase emanations
through inductive coupling from the local power grid.

In 2017, Naija et al. [159] suggested a parallel design for the PRESENT block
cypher as a single method of concealing defenses against EM-SCA. While 10,000
EM traces are utilized to target a present-day serial architecture, 200,000 EM
traces are used to attack the suggested design. The authors then suggested
that the mutual authentication protocol implement a countermeasure by
gradually restricting the quantity of EM traces. This restriction makes it
impossible for the attacker to use EMA. When compared to current
countermeasures at primitive block cyphers (2,471 gate equivalents (GEs)), the
suggested countermeasure is based on a time delay function and only needs 960
GEs.

Furthermore, Patranabis et al. [158] addressed the issue of design-for-
security approaches to create compact block cyphers with combined defenses
against side-channel and fault attacks for IoT applications. They chose FPGAs
as their target platforms because of the variety of programmable features that
make them ideal for IoT. Three key design ideas were brought forward, the
first dealing with side-channel protection and the other with fault attack
defense. Recursive design strategies for MDS linear layers with lightweight
roots are suggested with regard to fault attacks. Such linear layers serve the
dual functions of establishing Fault Space Transformation (FST) for defending
against fault attacks with characteristics similar to DFA and differential
fault intensity attacks (DFIA) and providing diffusion for resistance against
classical cryptanalysis. Combining two lightweight strategies—masking with
periodic refresh and shuffling over several rounds—was assessed in the context
of side-channel protection.

In 2022, Judy et al. [218] look at the most recent advancements in SCA and
their defenses. The prominent encryption system AES is implemented in
hardware, and the research primarily focuses on the direct interpretation of
the EM output. The authors examined two strategies for preventing side-channel
attacks—physical shielding and software-based interruption—using an ATMEGA8
AES implementation in their studies. The findings demonstrated that physical
shielding provided some signal attenuation, but that the attack might still
succeed with higher trace counts. On the other hand, the software-based
interruption strategy improved the mean time of failure by more successfully
interfering with the synchronization of the side-channel attack and obscuring
its characterization.

In 2023, Gao et al. [219] presented a new defensive method for guarding
against both intrusive and non-intrusive EM-SCA. An active shield and a module
to obfuscate information leaking make up the suggested framework. The top
metal layer of the chip is used to implement a random Hamiltonian topology
method known as the Artificial Fish-Swarm Random Hamiltonian Algorithm
(AFSRHA), which is used to construct the active shield. The information
leakage obfuscation module keeps track of intrusive attacks and sends out
signals to the wire mesh to increase EM emission and strengthen the wire
mesh’s defense against SCA. Through simulated studies, the suggested EO-shield
system’s security was proven, and it was demonstrated to be capable of
deterring intrusive attacks and lowering the correlation between EM emanations
and data processing. The authors argue that in order to provide enough
protection, the active shield does not necessarily need to comprise the entire
top metal layer. The EO-shield scheme presents a promising solution for
protecting against EM-SCA and provides a multidimensional approach by
combining an active shield with an information leakage obfuscation module.

##  VII Discussion

Having conducted a thorough review of the extant literature pertaining to EM-
SCA on encryption, it is imperative to determine the various conditions under
which EM-SCA can be employed in different scenarios. This section delves into
an in-depth examination of the three research questions that have been
identified under different themes.

###  VII-A RQ1: What is the state of the art of EM-SCA with respect to
attacking encryption?

The state of the art of EM-SCA with respect to attacking encryption is
constantly evolving. Researchers continue to develop new methods and
techniques for performing EM side-channel attacks, and they are becoming
increasingly sophisticated. Research on EM-SCA on encryption algorithms began
in the 1990s. However, the first published research paper on the topic is
“Power Analysis of Key Schedules” by Paul Kocher, Joshua Jaffe, and Benjamin
in 1999 [190]. This paper presented a method for performing a side-channel
attack on the key schedule of the DES algorithm using EM emissions. EM-SCA
exploit the EM emissions that are generated by a device as it performs
cryptographic operations. These emissions can include EM radiation, electric
currents, and magnetic fields. By analyzing these emissions, an attacker can
infer information about the cryptographic operations being performed, such as
the secret key being used.

Power analysis can measure the electrical power consumption of a device as it
encrypts data [9, 47, 117]. Information about the secret key being used can be
deduced by analyzing the power consumption patterns. Similarly, the EM
radiation that a device emits as it encrypts data has been measured via EM-SCA
analysis, which has been used extensively in publications. The analysis of EM
emissions can reveal information about the secret key.

One of the most advanced EM-SCA techniques mention previously is called
“template attacks”, where an attacker uses a set of known plaintext-ciphertext
pairs to create a template that can be used to infer the secret key used for
encryption [199, 115]. These template attacks are often quite successful
against hardware-implemented advanced encryption algorithms, as shown by
Bukasa et al. [199]. Machine learning-based attacks have been applied as a
cutting-edge method that involves utilizing machine learning algorithms to
examine the EM emissions produced by a device as it executes cryptographic
operations [80, 100, 113, 117]. These machine learning-based attacks can be
particularly successful since they automatically recognize EM emission
patterns that signify the use of the encryption key. Additionally, “power
analysis attacks”, whereby the attacker monitors the electrical power usage of
a device while it encrypts data and utilizes this data to deduce information
about the secret key, remains a well-liked technique for conducting EM-SCA.

Finally, it should be noted that the state of the art for EM-SCA on encryption
is constantly changing and that new approaches and strategies are continually
being developed. Even the most cutting-edge encryption algorithms and
implementations can be subject to EM-SCA, thus it’s crucial to bear this in
mind while designing and putting into practice cryptographic systems.

###  VII-B RQ2: Which encryption algorithms and encrypted devices are most
susceptible to, and resilient against, EM-SCA attacks?

Since EM-SCA attacks take advantage of the EM emissions produced by a device
as it executes cryptographic operations, all encryption algorithms and devices
are theoretically vulnerable to them. Nevertheless, some encryption methods
and devices are more prone to them than others.

Symmetric encryption algorithms, i.e., AES and DES, are particularly
vulnerable to EM side-channel attacks since they require a secret key to both
encrypt and decrypt data [92, 94, 167]. EM-SCA can be used by attackers to
extract information about the secret key, and examples of devices that
implement DES and AES, and thus are vulnerable, e.g., smartphones [94],
laptops/computers [30], and other portable devices that use encryption to
protect sensitive data. The AES algorithm is more effective compared to DES
and can process larger block sizes of plaintext with low power dissipation and
fast processing time. The AES has a higher potential for implementation in IoT
systems [220, 221, 204], especially in sensor-based applications. However, the
AES implementation is still vulnerable to SCA, particularly the CEMA attack
[107, 222, 223]. The AES-128 algorithm requires 10 rounds of iterations to
encrypt the plaintext, making it highly effective compared to other encryption
algorithms such as DES and triple-DES. Despite its effectiveness, the AES
implementation may still be vulnerable to side-channel attacks due to the
leakage of physical parameters that correlate with intermediate data, as
demonstrated in the literature.

On the other hand, EM-SCA can also be carried out against public-key
encryption algorithms i.e., RSA and ECC, albeit these attacks are typically
more challenging to carry out than attacks against symmetric encryption
algorithms. However, Goller and Sigl [103] demonstrate the square-and-multiply
approach to attack a reference software RSA implementation and Belgarric et
al. [198] performed EM analysis to pinpoint ECC addition and multiplication
processes in the Bouncy Castle ECDSA implementation for Android. Examples of
these types of devices include smartcards [9, 165], hardware security modules
[224, 126], and other devices that use public-key encryption to protect
sensitive data. In addition, there is a gap in the literature on the use of
EM-SCA for EEA encryption, providing an avenue for future research to evaluate
the effectiveness of EM-SCA attacks. However, no studies specifically assess
the efficacy of EM-SCA attacks on EEA encryption. It is crucial to emphasize
the necessity of further research to comprehend better the limitations and
vulnerabilities of EEA encryption against EM-SCA attacks. The lack of
information hinders law enforcement and digital forensic investigations, which
struggle to access EEA-encrypted devices due to the limited literature on EM-
SCA. More investigation is necessary to improve the understanding of EM-SCA
attacks on EEA encryption and develop better countermeasures.

In addition, when compared to hardware-based systems, software-based devices
are typically more vulnerable to EM side-channel attacks [103, 199]. This is
thus because software implementations frequently produce higher EM emissions
than hardware does. Due to their frequent insufficient physical security
features and restricted computing capacity, smartcards, RFID tags, and other
small, portable devices are also especially susceptible to EM side-channel
attacks.

Furthermore, since EM-SCA attacks take advantage of EM emissions produced by a
device while it executes cryptographic operations, no encryption algorithm can
be claimed to be totally resistant to them. Every encryption process produces
some form of EM emissions. To be more resistant to EM-SCA than others, several
encryption algorithms and devices have been developed. The masking technique,
which is utilized to hide the delicate cryptographic processes in the circuit,
is one case of an encryption algorithm that has been developed to be more
resistant to EM-SCA [181, 225, 226]. By reducing the quantity of EM emissions
produced by the cryptographic processes, this strategy can make it more
challenging for an attacker to deduce information regarding the secret key. In
terms of devices, hardware implementations are typically thought to be more
resistant to EM-SCA than software implementations, since they typically
produce fewer EM emissions. Devices that have been built with physical
security features, including tamper-proof enclosures, can also be more
resistant to EM-SCA.

The vulnerability of encryption algorithms to EM-SCA can fluctuate over time,
since the state-of-the-art in these attacks is always advancing. Furthermore,
a cryptographic system’s precise implementation characteristics may have an
impact on how vulnerable a certain encryption technique is. Similarly, the
resilience of a device can also depend on the precise implementation details
of the cryptographic system. It is necessary to note that an encryption
algorithm’s or a device’s resilience to EM-SCA can vary over time.

###  VII-C RQ3: Which approaches to EM-SCA on encryption prove most fruitful,
or demonstrates the most promise for the future?

EM-SCA exploit information leaked through EM emanations from a device, i.e.,
as power consumption or EM radiation, to extract secret information, i.e., as
encryption keys. Although there are several EM-SCA techniques that have been
demonstrated in the literature, most of them appear to be employable under
ideal circumstances, for example, using Faraday cages to suppress noise or
selecting the most effective victim devices for an attack. It is important to
investigate which EM-SCA approaches are more realistic and practically usable
under real-world conditions. One such method utilizes live data forensic
techniques to look into encrypted and powered-on devices [11]. A strategy like
this may compel the victim device to carry out cryptographic operations, which
could then be seen using EM emissions to extract the key. However, this method
may not be practical for many types of personal devices, such as those that
employ SSL-based online traffic and infrequently execute cryptographic
operations. According to the literature, using several side-channel attacks to
target a single computer system can be more effective than utilizing one side-
channel attack alone. Another practical approach for EM-SCA attacks involves
the use of fault injection techniques to introduce faults into the
cryptographic device’s operation, which can be monitored using EM emissions.
This approach has been demonstrated in several studies and has been shown to
be successful in recovering encryption keys, e.g., FIA and EM-SCA can be
combined to get superior results, as has been demonstrated [129].

Modern SoC processors used in smartphones include many cores that can execute
several threads at once [2]. As a result, the device can run many exhaustively
significant software operations concurrently, such as encrypting data stored
in non-volatile storage and carrying out a wireless network connection. The
number of concurrent activities grows further as the number of cores
increases. EM radiation can be seen, and EM trace data can be gathered as long
as the CPU cores operate at the same clock frequency. Similarly, the usage of
DVFS methods in contemporary CPUs [227] is another potential issue that
contributes to this predicament. It enables a SoC processor to change its
clock rates dynamically in response to workload. These methods require for the
simultaneous observation of EM radiation at several frequencies, as well as
the ability to spontaneously and dynamically modify the frequency of the
signal being observed. One prominent example of the impact of DVFS on side-
channel attacks is the VoltJockey exploit, which was proposed by [128]. It
represents a cutting-edge, fault-based attack that targets the multicore CPUs’
DVFS capabilities. According to these findings, the use of DVFS in modern CPUs
poses a serious risk to the security of mobile devices, especially when
combined with side-channel attacks such as EM radiation analysis. Thus, the
discovery of possible side-channel attack vulnerabilities in contemporary
smartphones has significant implications for law enforcement and digital
investigators. However, due to the complexity of these attacks, it is
essential to have the knowledge and tools required to carry them out
successfully. With appropriate training and tools, these agencies can access
encrypted data while maintaining the security and privacy of individuals and
organizations. It is crucial to stress that these attacks must only be used in
appropriate situations and compliance with relevant laws and regulations.

In addition, recent developments in AI and DL have shown significant uses for
implementing EM-SCA. To be more precise, the analysis of EM-SCA using deep
learning techniques i.e., CNN, DNN, MTL and MLP, has produced encouraging
results that have been published. These methods can be trained to detect
patterns in EM signals that signify specific encryption keys or algorithms,
making them effective in exploiting side-channel leakages. Besides, applying
CNN and DNN algorithms that can adjust to the changing EM environment and
learn to extract sensitive information from it has previously demonstrated
promising results. On top of that, the literature suggests that GAN can be
employed to generate more data, thereby improving the results of EM-SCA.
Another promising approach involves using transfer learning techniques to pre-
train machine learning models on a large dataset of EM signals, and then fine-
tuning the model for specific encryption algorithms. To summarize, utilizing
deep learning techniques i.e., CNN, DNN, and MTL, in EM-SCA on encryption can
be considered the most promising approach for the future. These advancements
provide a significant opportunity to enhance the effectiveness of EM-SCA in
attacking encryption, making it an effective mechanism for digital forensics
and law enforcement.

##  VIII Conclusion and Future Directions

The state of the art of EM-SCA with respect to attacking encryption is ever-
evolving, and new methods are being constantly developed. EM-SCAs are a major
concern for the security of cryptographic systems. These attacks take
advantage of the EM emissions generated by a device as it performs
cryptographic operations to infer information about the secret key used for
encryption. The evolution of EM-SCA techniques has led to a wide range of
successful attacks, from power analysis attacks to machine learning-based
attacks. Symmetric encryption algorithms like AES and DES are particularly
vulnerable to EM-SCA, while public-key encryption algorithms like RSA are also
subject to these attacks, albeit to a lesser degree. Power analysis, which
counts the amount of electricity used by a device to encrypt data, is one of
the earliest techniques for carrying out EM-SCA attacks. The secret key’s
information can then be determined using this data on power use. Other EM-SCA
techniques include template attacks, which use well-known plaintext-ciphertext
pairs to create a template from which the secret key can be deduced, and
machine learning-based attacks, which use machine learning algorithms to look
at the EM emissions generated by a device as it executes cryptographic
operations.

Although many EM-SCA techniques in the literature require ideal conditions,
i.e., using Faraday cages or selecting specific victim devices, practical
approaches using live data forensic technique can be utilized to investigate
encrypted and powered-on devices. This can compel the device to perform
cryptographic operations that can be analyzed using EM emissions. Another
practical approach is using fault injection techniques to introduce fault into
the cryptographic device’s operation, which can be monitored using EM
emissions and has successfully recovered encryption keys. Additionally, using
multiple side-channel attacks to target a single computer or digital device
system can be more effective than using a single side-channel attack.
Furthermore, machine learning algorithms show promise in the future of EM-SCA
research. Future research can focus on developing effective strategies for
ransomware disaster recovery and data recover, e.g., EM-SCA can be used to
extract the key, eliminating the need to pay the ransom.

Advancing this argument and conducting further research is necessary to
improve the understanding of the limitations and vulnerabilities of encryption
in the face of EM-SCA attacks. Several open questions require further
exploration, i.e., determining the most practical and realistic EM-SCA
approaches under real-world conditions, including law enforcement and digital
forensic needs. In addition, EEA encryption has received little attention in
the literature, and further investigation is necessary to understand its
vulnerabilities against EM-SCA attacks better. Moreover, Deep Learning-based
attacks, e.g., CNN, DNN, MTL, and MLP, have the ability to identify patterns
in EM signals that indicate specific encryption keys or algorithms, showing
potential for use in digital forensic and law enforcement applications. To
further advance this research area, new techniques and approaches should be
developed to investigate the use of EM-SCA for ransomware disaster recovery
and data recovery. This approach can be particularly useful in cases where
traditional data recovery methods are not feasible, e.g., when the encrypted
data has been overwritten or deleted. However, more research is required to
investigate this approach’s effectiveness and develop techniques for practical
implementation in real-world scenarios. Additionally, exploring machine
learning-based attacks and utilizing EM-SCA in law enforcement and digital
forensics are critical areas of research. Therefore, further research is
needed to address unanswered questions and challenges in these areas to
enhance the effectiveness of EM-SCA in combating encryption and supporting law
enforcement and digital forensics efforts.

As EM-SCA techniques continue to advance, it is crucial to consider the
security of cryptographic systems during design and implementation. To this
end, numerous software and hardware countermeasures have been proposed and
developed to protect against these attacks, including masking techniques,
cryptographic countermeasures, and the physical separation of analogue and
digital components. However, these countermeasures may have a negative effect
on performance and may be challenging to implement without compromising other
requirements like cost and chip size. Consequently, the future direction of
EM-SCA research should focus on developing more effective countermeasures that
are secure and efficient. This includes the exploration of new hardware-based
countermeasures, the improvement of existing software-based countermeasures,
and the integration of these countermeasures into cryptographic systems in a
way that does not negatively impact performance. Moreover, the development of
new EM-SCA attack techniques will likely continue, making it important for the
community to stay updated on the latest advancements and continuously improve
the security of cryptographic systems.

In addition, quantum computing can potentially transform the computing
industry due to its ability to solve issues that conventional computers are
currently unable to handle. Quantum computers will be able to break the
encryption used to protect sensitive data as they develop in capability, which
will jeopardize system security [228]. This has ramifications for those who
depend on encryption to protect their personal information as well as for
sectors including finance, healthcare, and national security. The influence of
quantum computing on public-key cryptography, the foundation of contemporary
internet security, is one of its most important consequences. The foundation
of public-key cryptography is the inefficiency of finding discrete logarithms
or large-number factors—both of which can be solved exponentially more quickly
on a quantum computer. This indicates that a sufficiently powerful quantum
computer could decrypt data using the popular AES, DES, and ECC encryption
techniques. An extensive body of literature has been dedicated to the pursuit
of developing quantum-resistant encryption techniques. This is due to the
advent of quantum computing and its potential to disrupt traditional
encryption methods. Several quantum-resistant encryption algorithms are
currently under investigation, including Lattice-based cryptography [229],
Multivariate cryptography [230], and code-based cryptography [231]. These
algorithmic developments are aimed at providing a secure encryption mechanism
in the post-quantum era, specifically designed to withstand attacks from
quantum computers. As a result, the field of cryptography and system security
will be significantly impacted by the development of quantum computing. As the
advancements in quantum computing pose a threat to the current encryption
algorithms, it is imperative for the future development of new quantum-
resistant encryption methods and quantum-powered encryption. Nonetheless, it
is important to note that even quantum encryption algorithms may still be
vulnerable to various EM-SCA approaches [232]. This underscores the need for
individuals and organizations to be cognizant of the potential impacts of
quantum computing on their systems and data.

As encryption becomes more prevalent in daily lives, digital forensic
investigators face increasing difficulties accessing and recovering digital
evidence from encrypted devices. Strong encryption cannot be bypassed without
a key or passphrase, leaving investigators with the decision to perform a live
forensic acquisition. Therefore, it is essential for researchers and
practitioners in the field of cryptography to ensure that future quantum-
facilitated cryptographic systems they develop and implement are resistant to
EM-SCA. Furthermore, it is imperative to develop new techniques and approaches
to break encryption, investigate how EM-SCA can be used for disaster recovery
and data recovery, and explore machine learning-based attacks. In this regard,
addressing the vulnerability of quantum encryption algorithms to EM-SCA
approaches can equip digital forensic investigators with the necessary tools
to overcome the challenges posed by strong encryption, enabling them to
continue playing a critical role in law enforcement and the justice system.
Thus, it is vital to continue research in this area to ensure that digital
forensic investigators can fulfill their role in the justice system while
maintaining the privacy and security of electronic devices and data.

## References

  * Zhou and Feng [2005] Y. Zhou and D. Feng, “Side-channel attacks: Ten years after its publication and the impacts on cryptographic module security testing,” Cryptology ePrint Archive, Paper 2005/388, 2005, https://eprint.iacr.org/2005/388. [Online]. Available: https://eprint.iacr.org/2005/388
  * Sayakkara et al. [2019a] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics,” _Digital Investigation_ , vol. 29, pp. 43–54, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287618303840
  * Das et al. [2020] D. Das, M. Nath, S. Ghosh, and S. Sen, “Killing EM side-channel leakage at its source,” _2020 IEEE 63rd International_ , 2020. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9184657/
  * Sayakkara et al. [2019b] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices,” _Digital Investigation_ , vol. 29, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287619301616
  * Getz and Moeckel [1996] R. Getz and B. Moeckel, “Understanding and eliminating emi in microcontroller applications,” _National Semiconductor_ , 1996.
  * Quisquater and Samyde [2001] J.-J. Quisquater and D. Samyde, “Electromagnetic analysis (ema): Measures and counter-measures for smart cards,” in _Proceedings of the International Conference on Research in Smart Cards: Smart Card Programming and Security_ , ser. E-SMART ’01. Berlin, Heidelberg: Springer-Verlag, 2001, p. 200–210.
  * Du et al. [2020] X. Du, C. Hargreaves, J. Sheppard, F. Anda, A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “Sok: Exploring the state of the art and the future potential of artificial intelligence in digital forensic investigation,” in _Proceedings of the 15th International Conference on Availability, Reliability and Security_ , ser. ARES ’20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3407023.3407068
  * De Mulder et al. [2005] E. De Mulder, P. Buysschaert, S. Ors, P. Delmotte, B. Preneel, G. Vandenbosch, and I. Verbauwhede, “Electromagnetic analysis attack on an fpga implementation of an elliptic curve cryptosystem,” in _EUROCON 2005 - The International Conference on ”Computer as a Tool”_ , vol. 2, 2005, pp. 1879–1882.
  * Gandolfi et al. [2001] K. Gandolfi, C. Mourtel, and F. Olivier, “Electromagnetic analysis: Concrete results,” in _Cryptographic Hardware and Embedded Systems — CHES 2001_. Springer Berlin Heidelberg, 2001, pp. 251–261.
  * Danial et al. [2020] J. Danial, D. Das, S. Ghosh, A. Raychowdhury, and S. Sen, “Scniffer: Low-cost, automated, efficient electromagnetic side-channel sniffing,” _IEEE Access_ , vol. 8, pp. 173 414–173 427, 2020.
  * Sayakkara et al. [2018a] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “Electromagnetic Side-channel Attacks: Potential for Progressing Hindered Digital Forensic Analysis,” in _Companion Proceedings for the ISSTA/ECOOP 2018 Workshops_. Association for Computing Machinery, 2018, p. 138. [Online]. Available: https://doi.org/10.1145/3236454.3236512
  * Casey et al. [2011] E. Casey, G. Fellows, M. Geiger, and G. Stellatos, “The growing impact of full disk encryption on digital forensics,” _Digital Investigation_ , vol. 8, no. 2, pp. 129–134, 2011, standards, professionalization and quality in digital forensics. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287611000727
  * Lillis et al. [2016] D. Lillis, B. Becker, T. O’Sullivan, and M. Scanlon, “Current challenges and future research areas for digital forensic investigation,” 2016. [Online]. Available: https://arxiv.org/abs/1604.03850
  * Lou et al. [2021] X. Lou, T. Zhang, J. Jiang, and Y. Zhang, “A survey of microarchitectural side-channel vulnerabilities, attacks, and defenses in cryptography,” _ACM Comput. Surv._ , vol. 54, no. 6, jul 2021. [Online]. Available: https://doi.org/10.1145/3456629
  * Kitchenham [2004] B. Kitchenham, “Procedures for performing systematic reviews,” _Keele University Technical Report TR/SE-0401_ , 2004.
  * Harzing [2023] A.-W. Harzing, “Publish or perish,” https://harzing.com/resources/publish-or-perish, 2023, accessed on Jun 27, 2022.
  * Dechammakl [2018] Dechammakl, “Electromagnetic waves,” https://commons.wikimedia.org/wiki/File:Electromagnetic_waves.png, 2018, accessed: 2022-09-07.
  * Naren et al. [2020] Naren, A. Elhence, V. Chamola, and M. Guizani, “Notice of retraction: Electromagnetic radiation due to cellular, wi-fi and bluetooth technologies: How safe are we?” _IEEE Access_ , vol. 8, pp. 42 980–43 000, 2020.
  * Ishimaru [2017] A. Ishimaru, _Electromagnetic wave propagation, radiation, and scattering: from fundamentals to applications_. John Wiley and Sons, 2017.
  * Rizvi et al. [2022] S. Rizvi, M. Scanlon, J. McGibney, and J. Sheppard, “Application of artificial intelligence to network forensics: Survey, challenges and future directions,” _IEEE Access_ , vol. 10, pp. 110 362–110 384, 2022.
  * Asthana et al. [2017] S. Asthana, A. Megahed, and R. Strong, “A recommendation system for proactive health monitoring using iot and wearable technologies,” in _2017 IEEE International Conference on AI and Mobile Services (AIMS)_ , 2017, pp. 14–21.
  * Zamanian and Hardiman [2005] A. Zamanian and C. Hardiman, “Electromagnetic radiation and human health: A review of sources and effects,” _High Frequency Electronics_ , vol. 4, no. 3, pp. 16–26, 2005.
  * Hayashi and Homma [2019] Y.-i. Hayashi and N. Homma, “Introduction to electromagnetic information security,” _IEICE Transactions on Communications_ , vol. 102, no. 1, pp. 40–50, 2019.
  * Ott [2011] H. W. Ott, _Electromagnetic Compatibility Engineering_. Wiley, 2011. [Online]. Available: https://books.google.ie/books?id=2-4WJKxzzigC
  * Bito et al. [2017] J. Bito, R. Bahr, J. G. Hester, S. A. Nauroze, A. Georgiadis, and M. M. Tentzeris, “A novel solar and electromagnetic energy harvesting system with a 3-d printed package for energy efficient internet-of-things wireless sensors,” _IEEE Transactions on Microwave Theory and Techniques_ , vol. 65, no. 5, pp. 1831–1842, 2017.
  * Poulin and Amiot [2002] F. Poulin and L.-P. Amiot, “Interference during the use of an electromagnetic tracking system under or conditions,” _Journal of Biomechanics_ , vol. 35, no. 6, pp. 733–737, 2002.
  * Longo et al. [2015a] J. Longo, E. D. Mulder, D. Page, and M. Tunstall, “SoC it to EM: electromagnetic side-channel attacks on a complex system-on-chip,” 2015. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-662-48324-4_31
  * Cheffena [2016] M. Cheffena, “Propagation channel characteristics of industrial wireless sensor networks [wireless corner],” _IEEE Antennas and Propagation Magazine_ , vol. 58, no. 1, pp. 66–73, 2016.
  * Lapinsky and Easty [2006] S. E. Lapinsky and A. C. Easty, “Electromagnetic interference in critical care,” _Journal of Critical Care_ , vol. 21, no. 3, pp. 267–270, 2006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0883944106000499
  * Sayakkara et al. [2018b] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “Accuracy Enhancement of Electromagnetic Side-Channel Attacks on Computer Monitors,” in _Proceedings of the 13th International Conference on Availability, Reliability and Security_. Association for Computing Machinery, 2018. [Online]. Available: https://doi.org/10.1145/3230833.3234690
  * Song et al. [2015] T.-L. Song, Y.-R. Jeong, and J.-G. Yook, “Modeling of leaked digital video signal and information recovery rate as a function of snr,” _IEEE Transactions on Electromagnetic Compatibility_ , vol. 57, no. 2, pp. 164–172, 2015.
  * Radasky et al. [2004] W. A. Radasky, C. E. Baum, and M. W. Wik, “Introduction to the special issue on high-power electromagnetics (hpem) and intentional electromagnetic interference (iemi),” _IEEE Transactions on Electromagnetic Compatibility_ , vol. 46, no. 3, pp. 314–321, 2004.
  * Rahimpour et al. [2021] S. Rahimpour, M. Kiyani, S. E. Hodges, and D. A. Turner, “Deep brain stimulation and electromagnetic interference,” _Clinical Neurology and Neurosurgery_ , vol. 203, p. 106577, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0303846721001049
  * van Eck [1985] W. van Eck, “Electromagnetic radiation from video display units: An eavesdropping risk?” _Computers and Security_ , vol. 4, no. 4, pp. 269–286, 1985. [Online]. Available: https://www.sciencedirect.com/science/article/pii/016740488590046X
  * Kuhn [2005] M. G. Kuhn, “Electromagnetic eavesdropping risks of flat-panel displays,” in _Privacy Enhancing Technologies_ , D. Martin and A. Serjantov, Eds. Springer Berlin Heidelberg, 2005, pp. 88–107.
  * Cobb et al. [2012] W. E. Cobb, E. D. Laspe, R. O. Baldwin, M. A. Temple, and Y. C. Kim, “Intrinsic physical-layer authentication of integrated circuits,” _IEEE Transactions on Information Forensics and Security_ , vol. 7, no. 1, pp. 14–24, 2012.
  * Maxwell [1865] J. C. Maxwell, “A dynamical theory of the electromagnetic field,” _Philosophical Transactions of the Royal Society of London_ , vol. 155, pp. 459–512, 1865. [Online]. Available: http://www.jstor.org/stable/108892
  * Lavaud et al. [2021] C. Lavaud, R. Gerzaguet, M. Gautier, O. Berder, and …, “Whispering devices: A survey on how side-channels lead to compromised information,” _Journal of Hardware and_ , 2021. [Online]. Available: https://link.springer.com/article/10.1007/s41635-021-00112-6
  * Xu et al. [2010] J. L. Xu, W. Su, and M. Zhou, “Software-defined radio equipped with rapid modulation recognition,” _IEEE Transactions on Vehicular Technology_ , vol. 59, no. 4, pp. 1659–1667, 2010.
  * Machado and Wyglinski [2015] R. G. Machado and A. M. Wyglinski, “Software-defined radio: Bridging the analog–digital divide,” _Proceedings of the IEEE_ , vol. 103, no. 3, pp. 409–423, 2015.
  * Osmann [2020] M. Osmann, “Software defined radio with hackrf,” 2020, accessed: 2022-07-21. [Online]. Available: https://greatscottgadgets.com/sdr
  * Martoyo et al. [2018] I. Martoyo, P. Setiasabda, H. Y. Kanalebe, H. P. Uranus, and M. Pardede, “Software defined radio for education: Spectrum analyzer, fm receiver/transmitter and gsm sniffer with hackrf one,” in _2018 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME)_ , 2018, pp. 188–192.
  * Kularatna [2003] N. Kularatna, _Digital and Analogue Instrumentation: Testing and Measurement_. Institution of Engineering and Technology, 2003.
  * R. Vanathi [2019] S. C. R. Vanathi, “Side channel attacks in iaas and its defense mechanisms,” _International Journal of Engineering and Advanced Technology (IJEAT)_ , vol. 8, no. 3S, 2019. [Online]. Available: https://www.amrita.edu/publication/side-channel-attacks-in-iaas-and-its-defense-mechanisms/
  * Kocher [1996] P. C. Kocher, “Timing attacks on implementations of diffie-hellman, rsa, dss, and other systems,” in _Advances in Cryptology — CRYPTO ’96_ , N. Koblitz, Ed. Springer Berlin Heidelberg, 1996, pp. 104–113.
  * Kong et al. [2009] J. Kong, O. Aciicmez, J.-P. Seifert, and H. Zhou, “Hardware-software integrated approaches to defend against software cache-based side channel attacks,” in _2009 IEEE 15th International Symposium on High Performance Computer Architecture_ , 2009, pp. 393–404.
  * Won and Bhasin [2021] Y.-S. Won and S. Bhasin, “A systematic side-channel evaluation of black box aes in secure mcu: Architecture recovery and retrieval of puf based secret key,” in _2021 IEEE International Symposium on Circuits and Systems (ISCAS)_ , 2021, pp. 1–5.
  * Ueno et al. [2022] R. Ueno, K. Xagawa, Y. Tanaka, A. Ito, J. Takahashi, and N. Homma, “Curse of Re-encryption: A Generic Power/Em Analysis on Post-quantum Kems,” _IACR Transactions on Cryptographic Hardware and Embedded Systems_ , no. 1, Nov. 2022. [Online]. Available: https://tches.iacr.org/index.php/TCHES/article/view/9298
  * Lisovets et al. [2021] O. Lisovets, D. Knichel, T. Moos, and A. Moradi, “Let’s take it offline: Boosting brute-force attacks on iphone’s user authentication through sca,” _IACR Transactions on Cryptographic Hardware and Embedded Systems_ , vol. 3, pp. 496–519, 2021.
  * Anderson [2020] R. Anderson, _Security Engineering: A Guide to Building Dependable Distributed Systems_. Wiley, 2020.
  * Batina et al. [2019] L. Batina, S. Bhasin, D. Jap, and S. Picek, “CSI NN: Reverse Engineering of Neural Network Architectures through Electromagnetic Side Channel,” in _Proceedings of the 28th USENIX Conference on Security Symposium_ , ser. SEC’19. USA: USENIX Association, 2019, p. 515–532.
  * Moradi et al. [2011] A. Moradi, A. Barenghi, T. Kasper, and C. Paar, “On the vulnerability of fpga bitstream encryption against power analysis attacks: Extracting keys from xilinx virtex-ii fpgas,” in _Proceedings of the 18th ACM Conference on Computer and Communications Security_ , ser. CCS ’11. New York, NY, USA: Association for Computing Machinery, 2011, p. 111–124. [Online]. Available: https://doi.org/10.1145/2046707.2046722
  * Mayer-Sommer [2000] R. Mayer-Sommer, “Smartly analyzing the simplicity and the power of simple power analysis on smartcards,” in _Cryptographic Hardware and Embedded Systems — CHES 2000_ , Ç. K. Koç and C. Paar, Eds. Springer Berlin Heidelberg, 2000, pp. 78–92.
  * Sayakkara et al. [2020a] A. Sayakkara, L. Miralles-Pechuán, N.-A. Le-Khac, and M. Scanlon, “Cutting Through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection,” _Forensic Science International: Digital Investigation_ , vol. 32, p. 300927, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666281720300226
  * Aghaie and Moradi [2020] A. Aghaie and A. Moradi, “Ti-puf: Toward side-channel resistant physical unclonable functions,” _IEEE Transactions on Information Forensics and Security_ , vol. 15, pp. 3470–3481, 2020.
  * Japa et al. [2021] A. Japa, M. K. Majumder, S. K. Sahoo, R. Vaddi, and B. K. Kaushik, “Hardware security exploiting post-cmos devices: Fundamental device characteristics, state-of-the-art countermeasures, challenges and roadmap,” _IEEE Circuits and Systems Magazine_ , vol. 21, no. 3, pp. 4–30, 2021.
  * Tuttlebee [2002] W. H. Tuttlebee, _Software Defined Radio: Enabling Technologies_. John Wiley and Sons, 2002.
  * Mutlu [2017] O. Mutlu, “The rowhammer problem and other issues we may face as memory ecomes denser,” in _Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017_, 2017, pp. 1116–1121.
  * Kim et al. [2014] Y. Kim, R. Daly, J. Kim, C. Fallin, J. H. Lee, D. Lee, C. Wilkerson, K. Lai, and O. Mutlu, “Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,” _SIGARCH Comput. Archit. News_ , vol. 42, no. 3, p. 361–372, jun 2014. [Online]. Available: https://doi.org/10.1145/2678373.2665726
  * Mutlu and Kim [2020] O. Mutlu and J. S. Kim, “Rowhammer: A retrospective,” _IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems_ , vol. 39, no. 8, pp. 1555–1571, 2020.
  * Yağlikçi et al. [2021] A. G. Yağlikçi, M. Patel, J. S. Kim, R. Azizi, A. Olgun, L. Orosa, H. Hassan, J. Park, K. Kanellopoulos, T. Shahroodi, S. Ghose, and O. Mutlu, “Blockhammer: Preventing rowhammer at low cost by blacklisting rapidly-accessed dram rows,” in _2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)_ , 2021, pp. 345–358.
  * Kim et al. [2020] J. S. Kim, M. Patel, A. G. Yağlıkçı, H. Hassan, R. Azizi, L. Orosa, and O. Mutlu, “Revisiting rowhammer: An experimental analysis of modern dram devices and mitigation techniques,” in _2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)_ , 2020, pp. 638–651.
  * Gupta and Nisbet [2016] K. Gupta and A. Nisbet, “Memory forensic data recovery utilising ram cooling methods,” _Proceedings of 14th Australian Digital Forensics Conference_ , pp. 11–16, 2016.
  * Halderman et al. [2009] J. A. Halderman, S. D. Schoen, N. Heninger, W. Clarkson, W. Paul, J. A. Calandrino, A. J. Feldman, J. Appelbaum, and E. W. Felten, “Lest we remember: Cold-boot attacks on encryption keys,” _Commun. ACM_ , vol. 52, no. 5, p. 91–98, may 2009. [Online]. Available: https://doi.org/10.1145/1506409.1506429
  * Halevi and Saxena [2015] T. Halevi and N. Saxena, “Keyboard acoustic side channel attacks: Exploring realistic and security-sensitive scenarios,” _International Journal of Information Security_ , vol. 14, no. 5, pp. 443–456, 2015.
  * Zhu et al. [2014] T. Zhu, Q. Ma, S. Zhang, and Y. Liu, “Context-free attacks using keyboard acoustic emanations,” in _Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security_ , ser. CCS ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 453–464. [Online]. Available: https://doi.org/10.1145/2660267.2660296
  * Asonov and Agrawal [2004] D. Asonov and R. Agrawal, “Keyboard acoustic emanations,” in _IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004_ , 2004, pp. 3–11.
  * Sabra et al. [2018] M. Sabra, A. Maiti, and M. Jadliwala, “Keystroke inference using ambient light sensor on wrist-wearables: A feasibility study,” in _Proceedings of the 4th ACM Workshop on Wearable Systems and Applications_ , ser. WearSys ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 21–26. [Online]. Available: https://doi.org/10.1145/3211960.3211973
  * Shahrad et al. [2018] M. Shahrad, A. Mosenia, L. Song, M. Chiang, D. Wentzlaff, and P. Mittal, “Acoustic Denial of Service Attacks on Hard Disk Drives,” in _Proceedings of the 2018 Workshop on Attacks and Solutions in Hardware Security_. Association for Computing Machinery, 2018, p. 34. [Online]. Available: https://doi.org/10.1145/3266444.3266448
  * Savage [2015] N. Savage, “Visualizing sound,” _Commun. ACM_ , vol. 58, no. 2, p. 15–17, jan 2015. [Online]. Available: https://doi.org/10.1145/2693430
  * Davis et al. [2014] A. Davis, M. Rubinstein, N. Wadhwa, G. J. Mysore, F. Durand, and W. T. Freeman, “The visual microphone: Passive recovery of sound from video,” _ACM Transactions on Graphics_ , vol. 33, no. 4, jul 2014. [Online]. Available: https://doi.org/10.1145/2601097.2601119
  * Kanta et al. [2020a] A. Kanta, I. Coisel, and M. Scanlon, “A survey exploring open source intelligence for smarter password cracking,” _Forensic Science International: Digital Investigation_ , vol. 35, p. 301075, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666281720303723
  * Kanta et al. [2021] A. Kanta, S. Coray, I. Coisel, and M. Scanlon, “How viable is password cracking in digital forensic investigation? analyzing the guessability of over 3.9 billion real-world accounts,” _Forensic Science International: Digital Investigation_ , vol. 37, p. 301186, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666281721000949
  * Kanta et al. [2020b] A. Kanta, I. Coisel, and M. Scanlon, “Smarter password guessing techniques leveraging contextual information and osint,” in _2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)_ , 2020, pp. 1–2.
  * Scanlon et al. [2023] M. Scanlon, F. Breitinger, C. Hargreaves, J.-N. Hilgert, and J. Sheppard, “Chatgpt for digital forensic investigation: The good, the bad, and the unknown,” _Forensic Science International: Digital Investigation_ , vol. 46, p. 301609, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S266628172300121X
  * Cho et al. [2015] J.-S. Cho, Y.-S. Jeong, and S. O. Park, “Consideration on the brute-force attack cost and retrieval cost: A hash-based radio-frequency identification (rfid) tag mutual authentication protocol,” _Computers and Mathematics with Applications_ , vol. 69, no. 1, pp. 58–65, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0898122112001393
  * Wazid et al. [2013] M. Wazid, A. Katal, R. Goudar, D. P. Singh, A. Tyagi, R. Sharma, and P. Bhakuni, “A framework for detection and prevention of novel keylogger spyware attacks,” in _2013 7th International Conference on Intelligent Systems and Control (ISCO)_ , 2013, pp. 433–438.
  * Unger et al. [2015] N. Unger, S. Dechand, J. Bonneau, S. Fahl, H. Perl, I. Goldberg, and M. Smith, “Sok: Secure messaging,” in _2015 IEEE Symposium on Security and Privacy_ , 2015, pp. 232–249.
  * Pub [2001] N. F. Pub, “Announcing the advanced encryption standard (aes),” _Federal Information Processing Standards Publication_ , vol. 197, pp. 1–51, 2001.
  * Yu and Chen [2018] W. Yu and J. Chen, “Deep learning-assisted and combined attack: A novel side-channel attack,” _Electronics Letters_ , vol. 54, no. 19, pp. 1114–1116, 2018. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/el.2018.5411
  * Lerman et al. [2015] L. Lerman, G. Bontempi, and O. Markowitch, “A machine learning approach against a masked aes,” _Journal of Cryptographic Engineering_ , vol. 5, no. 2, pp. 123–139, 2015.
  * Yang et al. [2004] B. Yang, K. Wu, and R. Karri, “Scan based side channel attack on dedicated hardware implementations of data encryption standard,” in _2004 International Conferce on Test_ , 2004, pp. 339–344.
  * Standaert [2010] F.-X. Standaert, _Introduction to Side-Channel Attacks_. Boston, MA: Springer US, 2010, pp. 27–42. [Online]. Available: https://doi.org/10.1007/978-0-387-71829-3_2
  * Azarderakhsh et al. [2014] R. Azarderakhsh, K. U. Järvinen, and M. Mozaffari-Kermani, “Efficient algorithm and architecture for elliptic curve cryptography for extremely constrained secure applications,” _IEEE Transactions on Circuits and Systems I: Regular Papers_ , vol. 61, no. 4, pp. 1144–1155, 2014.
  * Mahmood et al. [2018] K. Mahmood, S. A. Chaudhry, H. Naqvi, S. Kumari, X. Li, and A. K. Sangaiah, “An elliptic curve cryptography based lightweight authentication scheme for smart grid communication,” _Future Generation Computer Systems_ , vol. 81, pp. 557–565, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X17309263
  * Fournaris et al. [2017] A. P. Fournaris, L. Papachristodoulou, and N. Sklavos, “Secure and efficient rns software implementation for elliptic curve cryptography,” in _2017 IEEE European Symposium on Security and Privacy Workshops (EuroS &PW)_, 2017, pp. 86–93.
  * Bikos and Sklavos [2013] A. N. Bikos and N. Sklavos, “Lte/sae security issues on 4g wireless networks,” _IEEE Security & Privacy_, vol. 11, no. 2, pp. 55–62, 2013.
  * Sulaiman and Al Shaikhli [2014] A. G. Sulaiman and I. F. Al Shaikhli, “Comparative study on 4g/lte cryptographic algorithms based on different factors,” _International Journal of Computer Science and Telecommunications_ , vol. 5, no. 7, pp. 7–10, 2014.
  * Barrett [2000] P. Barrett, “Implementing the rivest shamir and adleman public key encryption algorithm on a standard digital signal processor,” in _Advances in Cryptology—CRYPTO’86: Proceedings_. Springer, 2000, pp. 311–323.
  * Genkin et al. [2014a] D. Genkin, A. Shamir, and E. Tromer, “Rsa key extraction via low-bandwidth acoustic cryptanalysis,” in _Advances in Cryptology – CRYPTO 2014_. Springer Berlin Heidelberg, 2014, pp. 444–461.
  * Li et al. [2019] G. Li, V. Iyer, and M. Orshansky, “Securing aes against localized em attacks through spatial randomization of dataflow,” in _2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)_ , 2019, pp. 191–197.
  * Kumar et al. [2017] A. Kumar, C. Scarborough, A. Yilmaz, and M. Orshansky, “Efficient simulation of em side-channel attack resilience,” in _2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)_ , 2017, pp. 123–130.
  * Vasselle et al. [2019] A. Vasselle, P. Maurine, and M. Cozzi, “Breaking mobile firmware encryption through near-field side-channel analysis,” in _Proceedings of the 3rd ACM Workshop on Attacks and Solutions in Hardware Security Workshop_ , ser. ASHES’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 23–32. [Online]. Available: https://doi.org/10.1145/3338508.3359571
  * Haas and Aysu [2022] G. Haas and A. Aysu, “Apple vs. ema: Electromagnetic side channel attacks on apple corecrypto,” in _Proceedings of the 59th ACM/IEEE Design Automation Conference_ , ser. DAC ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 247–252. [Online]. Available: https://doi.org/10.1145/3489517.3530437
  * Haas et al. [2021] G. Haas, S. Potluri, and A. Aysu, “itimed: Cache attacks on the apple a10 fusion soc,” in _2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)_ , 2021, pp. 80–90.
  * Iyer and Yilmaz [2021] V. V. Iyer and A. E. Yilmaz, “Using the anova f-statistic to rapidly identify near-field vulnerabilities of cryptographic modules,” in _2021 IEEE MTT-S International Microwave Symposium (IMS)_ , 2021, pp. 112–115.
  * Burnside et al. [2008] A. Burnside, A. Erdogan, and T. Arslan, “The Re-emission Side Channel,” in _2008 Bio-inspired, Learning and Intelligent Systems for Security_ , 2008, pp. 154–159.
  * Sauvage et al. [2009] L. Sauvage, S. Guilley, and Y. Mathieu, “Electromagnetic radiations of fpgas: High spatial resolution cartography and attack on a cryptographic module,” _ACM Trans. Reconfigurable Technol. Syst._ , vol. 2, no. 1, mar 2009. [Online]. Available: https://doi.org/10.1145/1502781.1502785
  * Lellis et al. [2017] R. Lellis, R. I. Soares, and A. Souza, “An energy-based attack flow for temporal misalignment coutermeasures on cryptosystems,” in _2017 IEEE International Symposium on Circuits and Systems (ISCAS)_ , 2017, pp. 1–4.
  * Mukhtar et al. [2021] N. Mukhtar, A. Mehrabi, Y. Kong, and A. Anjum, “Edge enhanced deep learning system for iot edge device security analytics,” _Concurrency and Computation: Practice and Experience_ , p. e6764, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6764
  * Genkin et al. [2016a] D. Genkin, L. Pachmanov, I. Pipman, and E. Tromer, “Ecdh key-extraction via low-bandwidth electromagnetic attacks on pcs,” in _Topics in Cryptology - CT-RSA 2016_ , K. Sako, Ed. Cham: Springer International Publishing, 2016, pp. 219–235.
  * Genkin et al. [2016b] D. Genkin, L. Pachmanov, I. Pipman, E. Tromer, and Y. Yarom, “Ecdsa key extraction from mobile devices via nonintrusive physical side channels,” in _Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security_ , ser. CCS ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1626–1638. [Online]. Available: https://doi.org/10.1145/2976749.2978353
  * Goller and Sigl [2015] G. Goller and G. Sigl, “Side channel attacks on smartphones and embedded devices using standard radio equipment,” in _International Workshop on Constructive Side-Channel Analysis and Secure Design_. Springer, 2015, pp. 255–270.
  * Nakano et al. [2014] Y. Nakano, Y. Souissi, R. Nguyen, L. Sauvage, J.-L. Danger, S. Guilley, S. Kiyomoto, and Y. Miyake, “A pre-processing composition for secret key recovery on android smartphone,” in _Information Security Theory and Practice. Securing the Internet of Things: 8th IFIP WG 11.2 International Workshop, WISTP 2014, Heraklion, Crete, Greece, June 30–July 2, 2014. Proceedings 8_. Springer, 2014, pp. 76–91.
  * Kenworthy and Rohatgi [2012] G. Kenworthy and P. Rohatgi, “Mobile device security: The case for side channel resistance,” in _Proceedings of the Mobile Security Technologies Conference (MoST)_ , 2012.
  * Genkin et al. [2014b] D. Genkin, I. Pipman, and E. Tromer, “Get your hands off my laptop: Physical side-channel key-extraction attacks on pcs,” in _Cryptographic Hardware and Embedded Systems – CHES 2014_. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 242–260.
  * Pammu et al. [2016a] A. A. Pammu, K.-S. Chong, W.-G. Ho, and B.-H. Gwee, “Interceptive side channel attack on aes-128 wireless communications for iot applications,” in _2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)_ , 2016, pp. 650–653.
  * Ding et al. [2009] G.-l. Ding, Z.-x. Li, X.-l. Chang, and Q. Zhao, “Differential electromagnetic analysis on aes cryptographic system,” in _2009 Second Pacific-Asia Conference on Web Mining and Web-based Application_ , 2009, pp. 120–123.
  * Frieslaar and Irwin [2018] I. Frieslaar and B. Irwin, “Developing an electromagnetic noise generator to protect a raspberry pi from side channel analysis,” _SAIEE Africa Research Journal_ , vol. 109, no. 2, pp. 85–101, 2018.
  * Alam et al. [2021] M. Alam, B. Yilmaz, F. Werner, N. Samwel, A. Zajic, D. Genkin, Y. Yarom, and M. Prvulovic, “Nonce@once: A single-trace em side channel attack on several constant-time elliptic curve implementations in mobile platforms,” in _2021 IEEE European Symposium on Security and Privacy (EuroS &P)_, 2021, pp. 507–522.
  * Soares et al. [2011] R. Soares, N. Calazans, F. Moraes, P. Maurine, and L. Torres, “A robust architectural approach for cryptographic algorithms using gals pipelines,” _IEEE Design & Test of Computers_, vol. 28, no. 5, pp. 62–71, 2011.
  * Mittal et al. [2021] S. Mittal, H. Gupta, and S. Srivastava, “A survey on hardware security of dnn models and accelerators,” _Journal of Systems Architecture_ , vol. 117, p. 102163, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1383762121001168
  * Xiao et al. [2020] Y. Xiao, J. Xin, and Y. Shen, “Cnn based electromagnetic side channel attacks on soc,” _IOP Conference Series: Materials Science and Engineering_ , vol. 782, no. 3, p. 032055, 3 2020. [Online]. Available: https://dx.doi.org/10.1088/1757-899X/782/3/032055
  * Wang et al. [2020] R. Wang, H. Wang, and E. Dubrova, “Far field em side-channel attack on aes using deep learning,” in _Proceedings of the 4th ACM Workshop on Attacks and Solutions in Hardware Security_ , ser. ASHES’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 35–44. [Online]. Available: https://doi.org/10.1145/3411504.3421214
  * Camurati et al. [2020] G. Camurati, A. Francillon, and F.-X. Standaert, “Understanding screaming channels: From a detailed analysis to improved attacks,” _IACR Transactions on Cryptographic Hardware and Embedded Systems_ , pp. 358–401, 2020.
  * Benadjila et al. [2020] R. Benadjila, E. Prouff, R. Strullu, E. Cagli, and C. Dumas, “Deep learning for side-channel analysis and introduction to ascad database,” _Journal of Cryptographic Engineering_ , vol. 10, no. 2, pp. 163–188, 2020.
  * Yu et al. [2021] H. Yu, H. Shan, M. Panoff, and Y. Jin, “Cross-device profiled side-channel attacks using meta-transfer learning,” in _2021 58th ACM/IEEE Design Automation Conference (DAC)_ , 2021, pp. 703–708.
  * Jap et al. [2020] D. Jap, V. Yli-Mäyry, A. Ito, R. Ueno, S. Bhasin, and N. Homma, “Practical side-channel based model extraction attack on tree-based machine learning algorithm,” in _Applied Cryptography and Network Security Workshops_. Cham: Springer International Publishing, 2020, pp. 93–105.
  * Pan et al. [2021] H. Pan, L. Yang, H. Li, C.-W. You, X. Ji, Y.-C. Chen, Z. Hu, and G. Xue, “Magthief: Stealing private app usage data on mobile devices via built-in magnetometer,” in _2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)_ , 2021, pp. 1–9.
  * Moradi et al. [2019] M. Moradi, B. Van Acker, K. Vanherpen, and J. Denil, “Model-implemented hybrid fault injection for simulink (tool demonstrations),” in _Cyber Physical Systems. Model-Based Design_ , R. Chamberlain, W. Taha, and M. Törngren, Eds. Cham: Springer International Publishing, 2019, pp. 71–90.
  * Bhunia and Tehranipoor [2019] S. Bhunia and M. Tehranipoor, “Chapter 10 - physical attacks and countermeasures,” in _Hardware Security_. Morgan Kaufmann, 2019, pp. 245–290. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780128124772000150
  * Lim et al. [2020] S. Lim, J. Lee, and D.-G. Han, “Improved differential fault attack on lea by algebraic representation of modular addition,” _IEEE Access_ , vol. 8, pp. 212 794–212 802, 2020.
  * Gunathilake et al. [2022] N. A. Gunathilake, A. Al-Dubai, W. J. Buchanan, and O. Lo, “Electromagnetic side-channel attack resilience against present lightweight block cipher,” in _2022 6th International Conference on Cryptography, Security and Privacy (CSP)_ , 2022, pp. 51–55.
  * Hell and Westman [2020] M. Hell and O. Westman, “Electromagnetic side-channel attack on aes using low-end equipment,” _ECTI Transactions on Computer and Information Technology (ECTI-CIT)_ , vol. 14, no. 2, p. 139–148, Apr. 2020. [Online]. Available: https://ph01.tci-thaijo.org/index.php/ecticit/article/view/239925
  * Chusseau et al. [2014] L. Chusseau, R. Omarouayache, J. Raoult, S. Jarrix, P. Maurine, K. Tobich, A. Bover, B. Vrignon, J. Shepherd, T.-H. Le, M. Berthier, L. Rivière, B. Robisson, and A.-L. Ribotta, “Electromagnetic analysis, deciphering and reverse engineering of integrated circuits (e-mata hari),” pp. 1–6, 2014.
  * Nakamura et al. [2015] K. Nakamura, Y.-i. Hayashi, N. Homma, T. Mizuki, T. Aoki, and H. Sone, “Method for estimating fault injection time on cryptographic devices from em leakage,” in _2015 IEEE International Symposium on Electromagnetic Compatibility (EMC)_ , 2015, pp. 235–240.
  * Dehbaoui et al. [2013] A. Dehbaoui, A.-P. Mirbaha, N. Moro, J.-M. Dutertre, and A. Tria, “Electromagnetic glitch on the aes round counter,” in _International Workshop on Constructive Side-Channel Analysis and Secure Design_. Springer, 2013, pp. 17–31.
  * Qiu et al. [2019] P. Qiu, D. Wang, Y. Lyu, and G. Qu, “Voltjockey: Breaching trustzone by software-controlled voltage manipulation over multi-core frequencies,” in _Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security_ , ser. CCS ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 195–209. [Online]. Available: https://doi-org.ucd.idm.oclc.org/10.1145/3319535.3354201
  * Ravi et al. [2020] P. Ravi, S. Bhasin, S. S. Roy, and A. Chattopadhyay, “Drop by Drop you break the rock-Exploiting generic vulnerabilities in Lattice-based PKE/KEMs using EM-based Physical Attacks,” _Cryptology ePrint Archive_ , 2020. [Online]. Available: https://eprint.iacr.org/2020/549
  * Wang et al. [2021] M. Wang, V. V. Iyer, S. Xie, G. Li, S. K. Mathew, R. Kumar, M. Orshansky, A. E. Yilmaz, and J. P. Kulkarni, “Physical design strategies for mitigating fine-grained electromagnetic side-channel attacks,” in _2021 IEEE Custom Integrated Circuits Conference (CICC)_ , 2021, pp. 1–2.
  * Sayakkara and Le-Khac [2021] A. P. Sayakkara and N.-A. Le-Khac, “Electromagnetic side-channel analysis for iot forensics: Challenges, framework, and datasets,” _IEEE Access_ , vol. 9, pp. 113 585–113 598, 2021.
  * Chatterjee et al. [2019] B. Chatterjee, N. Cao, A. Raychowdhury, and S. Sen, “Context-aware intelligence in resource-constrained iot nodes: Opportunities and challenges,” _IEEE Design & Test_, vol. 36, no. 2, pp. 7–40, 2019.
  * Yoshida et al. [2019] K. Yoshida, T. Kubota, M. Shiozaki, and T. Fujino, “Model-extraction attack against fpga-dnn accelerator utilizing correlation electromagnetic analysis,” in _2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)_ , 2019, pp. 318–318.
  * Sosa et al. [2021] O. A. Sosa, Z. Dyka, I. Kabin, and P. Langendörfer, “Simulation of Electromagnetic Emanation of Cryptographic ICs: Tools, Methods, Problems,” in _2021 IEEE East-West Design & Test Symposium (EWDTS)_, 2021, pp. 1–5.
  * Belaïd et al. [2020] S. Belaïd, P.-É. Dagand, D. Mercadier, M. Rivain, and R. Wintersdorff, “Tornado: Automatic generation of probing-secure masked bitsliced implementations,” in _Advances in Cryptology – EUROCRYPT 2020_. Cham: Springer International Publishing, 2020, pp. 311–341.
  * Vosoughi et al. [2020] A. Vosoughi, L. Wang, and S. Köse, “Voltage regulator assisted lightweight countermeasure against fault injection attacks,” _arXiv preprint arXiv:2001.03230_ , 2020.
  * Dey et al. [2022] S. Dey, J. Park, N. Pundir, D. Saha, A. M. Shuvo, D. Mehta, N. Asadi, F. Rahman, F. Farahmandi, and M. Tehranipoor, “Secure physical design,” _Cryptology ePrint Archive_ , 2022. [Online]. Available: https://eprint.iacr.org/2022/891
  * Abdellatif [2019] K. M. Abdellatif, “Towards efficient alignment for electromagnetic side channel attacks,” in _2019 31st International Conference on Microelectronics (ICM)_ , 2019, pp. 118–121.
  * Kwon et al. [2021] D. Kwon, H. Kim, and S. Hong, “Non-profiled deep learning-based side-channel preprocessing with autoencoders,” _IEEE Access_ , vol. 9, pp. 57 692–57 703, 2021.
  * Iyer et al. [2021] V. V. Iyer, M. Wang, J. Kulkarni, and A. E. Yilmaz, “A systematic evaluation of em and power side-channel analysis attacks on aes implementations,” in _2021 IEEE International Conference on Intelligence and Security Informatics (ISI)_ , 2021, pp. 1–6.
  * Kumar et al. [2020] R. Kumar, V. Suresh, M. Kar, S. Satpathy, M. A. Anders, H. Kaul, A. Agarwal, S. Hsu, G. K. Chen, R. K. Krishnamurthy, V. De, and S. K. Mathew, “A 4900-mu m(2) 839-Mb/s Side-Channel Attack-Resistant AES-128 in 14-nm CMOS With Heterogeneous Sboxes, Linear Masked MixColumns, and Dual-Rail Key Addition,” _IEEE Journal of Solid-State Circuits_ , vol. 55, no. 4, pp. 945–955, 2020.
  * Smith [1997] S. W. Smith, _The Scientist and Engineer’s Guide to Digital Signal Processing_. California Technical Pub. San Diego, 1997.
  * Souissi et al. [2011] Y. Souissi, M. A. Elaabid, N. Debande, S. Guilley, and J.-L. Danger, “Novel applications of wavelet transforms based side-channel analysis,” in _Non-Invasive Attack Testing Workshop_ , 2011.
  * Zhou and Standaert [2019] Y. Zhou and F.-X. Standaert, “Simplified single-trace side-channel attacks on elliptic curve scalar multiplication using fully convolutional networks,” in _Proceedings of the 40th WIC Symposium on Information Theory in the Benelux_ , 2019. [Online]. Available: http://hdl.handle.net/2078.1/226275
  * Bu et al. [2018] A. Bu, W. Dai, M. Lu, H. Cai, and W. Shan, “Correlation-based electromagnetic analysis attack using haar wavelet reconstruction with low-pass filtering on an fpga implementaion of aes,” in _2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)_ , 2018, pp. 1897–1900.
  * Plos et al. [2009] T. Plos, M. Hutter, and M. Feldhofer, _On Comparing Side-Channel Preprocessing Techniques for Attacking RFID Devices_. Berlin, Heidelberg: Springer-Verlag, 2009, p. 163–177.
  * Longo et al. [2015b] J. Longo, E. De Mulder, D. Page, and M. Tunstall, “Soc it to em: Electromagnetic side-channel attacks on a complex system-on-chip,” in _Cryptographic Hardware and Embedded Systems–CHES 2015: 17th International Workshop, Saint-Malo, France, September 13-16, 2015, Proceedings 17_. Springer, 2015, pp. 620–640.
  * Arafat et al. [2021] A. A. Arafat, Z. Guo, and A. Awad, “Vr-spy: A side-channel attack on virtual key-logging in vr headsets,” in _2021 IEEE Virtual Reality and 3D User Interfaces (VR)_ , 2021, pp. 564–572.
  * Standaert and Archambeau [2008] F.-X. Standaert and C. Archambeau, “Using subspace-based template attacks to compare and combine power and electromagnetic information leakages,” in _Proceeding Sof the 10th International Workshop on Cryptographic Hardware and Embedded Systems_ , ser. CHES ’08. Berlin, Heidelberg: Springer-Verlag, 2008, p. 411–425. [Online]. Available: https://doi.org/10.1007/978-3-540-85053-3_26
  * Gao et al. [2018] Y. Gao, H. Zhang, W. Cheng, Y. Zhou, and Y. Cao, “Electro-magnetic analysis of gpu-based aes implementation,” in _Proceedings of the 55th Annual Design Automation Conference_ , ser. DAC ’18. New York, NY, USA: Association for Computing Machinery, 2018. [Online]. Available: https://doi.org/10.1145/3195970.3196042
  * Eisenbarth et al. [2007] T. Eisenbarth, S. Kumar, C. Paar, A. Poschmann, and L. Uhsadel, “A survey of lightweight-cryptography implementations,” _IEEE Design & Test of Computers_, vol. 24, no. 6, pp. 522–533, 2007.
  * Park and Kim [2014] M. Park and J. Kim, “Differential fault analysis of the block cipher lea,” _Journal of the Korea Institute of Information Security & Cryptology_, vol. 24, no. 6, pp. 1117–1127, 2014.
  * Jap and Breier [2015] D. Jap and J. Breier, “Differential fault attack on lea,” in _Information and Communication Technology_ , I. Khalil, E. Neuhold, A. M. Tjoa, L. D. Xu, and I. You, Eds. Cham: Springer International Publishing, 2015, pp. 265–274.
  * Nozaki et al. [2017] Y. Nozaki, T. Iwase, Y. Ikezaki, and M. Yoshikawa, “Differential electromagnetic analysis for present and its evaluation with several selection functions,” _Journal of International Council on Electrical Engineering_ , vol. 7, no. 1, pp. 137–141, 2017.
  * Le Bouder et al. [2016] H. Le Bouder, T. Barry, D. Couroussé, J. Lanet, and R. Lashermes, “A template attack against verify pin algorithms,” in _Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - SECRYPT, (ICETE 2016)_ , INSTICC. SciTePress, 2016, pp. 231–238.
  * Simon and Patel [2015] P. Simon and P. Patel, “Side channel analysis of sim cards using combined higher order statistical techniques,” in _International Conference on Cyber Warfare and Security_. Academic Conferences International Limited, 2015, p. 525.
  * Li [2015] W. Li, “An Ultra-Lightweight Side-Channel Resistant Crypto for Pervasive Devices,” _International Journal of Multimedia and Ubiquitous Engineering_ , vol. 10, pp. 173–186, 2015.
  * Patranabis et al. [2019] S. Patranabis, D. B. Roy, A. Chakraborty, N. Nagar, A. Singh, D. Mukhopadhyay, and S. Ghosh, “Lightweight Design-for-security Strategies for Combined Countermeasures Against Side Channel and Fault Analysis in IoT Applications,” _Journal of Hardware and Systems Security_ , 2019. [Online]. Available: https://link.springer.com/article/10.1007/s41635-018-0049-y
  * Naija et al. [2017] Y. Naija, V. Beroulle, and M. Machhout, “Low cost countermeasure at authentication protocol level against electromagnetic side channel attacks on rfid tags,” _International Journal of Advanced Computer Science and Applications_ , vol. 8, no. 11, 2017.
  * Yoshikawa et al. [2016] M. Yoshikawa, Y. Nozaki, and K. Asahi, “Electromagnetic analysis attack for a lightweight block cipher twine,” in _2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)_ , 2016, pp. 1–2.
  * Yoshikawa and Nozaki [2016] M. Yoshikawa and Y. Nozaki, “Electromagnetic analysis attack for a lightweight cipher prince,” in _2016 IEEE International Conference on Cybercrime and Computer Forensic (ICCCF)_ , 2016, pp. 1–6.
  * Kasper et al. [2012] T. Kasper, D. Oswald, and C. Paar, “Side-channel analysis of cryptographic rfids with analog demodulation,” in _RFID. Security and Privacy_ , A. Juels and C. Paar, Eds. Springer Berlin Heidelberg, 2012, pp. 61–77.
  * Plos et al. [2012] T. Plos, M. Aigner, T. Baier, M. Feldhofer, M. Hutter, T. Korak, and E. Wenger, “Semi-passive rfid development platform for implementing and attacking security tags,” _International Journal of RFID Security and Cryptography_ , vol. 1, no. 1, pp. 16–24, 2012.
  * Naija et al. [2018a] Y. Naija, V. Beroulle, and M. Machhout, “Security Enhancements of a Mutual Authentication Protocol used in a HF Full-fledged RFID Tag,” _Journal of Electronic Testing_ , vol. 34, pp. 291–304, 2018. [Online]. Available: https://link.springer.com/article/10.1007/s10836-018-5725-x
  * Carluccio et al. [2005] D. Carluccio, K. Lemke, and C. Paar, “Electromagnetic side channel analysis of a contactless smart card: First results,” in _ECrypt Workshop on RFID and Lightweight Crypto_ , 2005.
  * Naija et al. [2018b] Y. Naija, V. Beroulle, and M. Machhout, “Electromagnetic attack test platform for validating rfid tag architectures,” in _2018 6th International EURASIP Workshop on RFID Technology (EURFID)_ , 2018, pp. 1–7.
  * Kasper et al. [2009] T. Kasper, D. Oswald, and C. Paar, “Em side-channel attacks on commercial contactless smartcards using low-cost equipment,” in _International Workshop on Information Security Applications_. Springer, 2009, pp. 79–93.
  * Xu et al. [2018] R. Xu, L. Zhu, A. Wang, X. Du, K.-K. R. Choo, G. Zhang, and K. Gai, “Side-channel attack on a protected rfid card,” _IEEE Access_ , vol. 6, pp. 58 395–58 404, 2018.
  * Oren and Shamir [2007] Y. Oren and A. Shamir, “Remote password extraction from rfid tags,” _IEEE Transactions on Computers_ , vol. 56, no. 9, pp. 1292–1296, 2007.
  * Du et al. [2007] X. Du, Y. Xiao, M. Guizani, and H.-H. Chen, “An effective key management scheme for heterogeneous sensor networks,” _Ad Hoc Networks_ , vol. 5, no. 1, pp. 24–34, 2007, security Issues in Sensor and Ad Hoc Networks. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1570870506000412
  * Du et al. [2009] X. Du, M. Guizani, Y. Xiao, and H.-H. Chen, “Transactions papers a routing-driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks,” _IEEE Transactions on Wireless Communications_ , vol. 8, no. 3, pp. 1223–1229, 2009.
  * Hei et al. [2013] X. Hei, X. Du, S. Lin, and I. Lee, “Pipac: Patient infusion pattern based access control scheme for wireless insulin pump system,” in _2013 Proceedings IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies_ , 2013, pp. 3030–3038.
  * Wu et al. [2016] L. Wu, X. Du, and J. Wu, “Effective defense schemes for phishing attacks on mobile computing platforms,” _IEEE Transactions on Vehicular Technology_ , vol. 65, no. 8, pp. 6678–6691, 2016.
  * Cheng et al. [2017] Y. Cheng, X. Fu, X. Du, B. Luo, and M. Guizani, “A lightweight live memory forensic approach based on hardware virtualization,” _Information Sciences_ , vol. 379, pp. 23–41, 2017.
  * Kim et al. [2012] T. H. Kim, C. Kim, and I. Park, “Side channel analysis attacks using am demodulation on commercial smart cards with seed,” _Journal of Systems and Software_ , vol. 85, no. 12, pp. 2899–2908, 2012, self-Adaptive Systems. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S016412121200194X
  * Lu et al. [2010] Y. Lu, K. H. Boey, P. Hodgers, and M. O’Neill, “Seed masking implementations against power analysis attacks,” in _2010 IEEE Asia Pacific Conference on Circuits and Systems_ , 2010, pp. 1199–1202.
  * Hutter et al. [2007] M. Hutter, S. Mangard, and M. Feldhofer, “Power and em attacks on passive 13.56 mhz rfid devices,” in _Cryptographic Hardware and Embedded Systems - CHES 2007_ , P. Paillier and I. Verbauwhede, Eds. Springer Berlin Heidelberg, 2007, pp. 320–333.
  * Kasper et al. [2010] T. Kasper, I. von Maurich, D. Oswald, and C. Paar, “Cloning cryptographic rfid cards for 25$,” in _5th Benelux Workshop on Information and System Security. Nijmegen, Netherlands_ , 2010.
  * Behzad [2007] A. Behzad, _Wireless LAN radios: System Definition to Transistor Design_. John Wiley & Sons, 2007.
  * Chaman et al. [2018] A. Chaman, J. Wang, J. Sun, H. Hassanieh, and R. Roy Choudhury, “Ghostbuster: Detecting the presence of hidden eavesdroppers,” in _Proceedings of the 24th Annual International Conference on Mobile Computing and Networking_ , ser. MobiCom ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 337–351. [Online]. Available: https://doi.org/10.1145/3241539.3241580
  * Camurati et al. [2018] G. Camurati, S. Poeplau, M. Muench, T. Hayes, and A. Francillon, “Screaming channels: When electromagnetic side channels meet radio transceivers,” in _Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security_ , 2018, pp. 163–177.
  * Duan et al. [2015] L. Duan, Z. Hongxin, L. Qiang, Z. Xinjie, and H. Pengfei, “Electromagnetic Side-channel Attack Based on PSO Directed Acyclic Graph SVM,” _The Journal of China Universities of Posts and Telecommunications_ , vol. 22, no. 5, pp. 10–15, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1005888515606744
  * Hori et al. [2012] Y. Hori, T. Katashita, A. Sasaki, and A. Satoh, “Electromagnetic side-channel attack against 28-nm fpga device,” _Pre-proceedings of the 13th International Workshop on Information Security Applications (WISA 2012)_ , p. 84, 2012.
  * Das and Sen [2020] D. Das and S. Sen, “Electromagnetic and power side-channel analysis: Advanced attacks and low-overhead generic countermeasures through white-box approach,” _Cryptography_ , vol. 4, no. 4, 2020. [Online]. Available: https://www.mdpi.com/2410-387X/4/4/30
  * Yilmaz et al. [2020] B. B. Yilmaz, M. Prvulovic, and A. Zajić, “Electromagnetic side channel information leakage created by execution of series of instructions in a computer processor,” _IEEE Transactions on Information Forensics and Security_ , vol. 15, pp. 776–789, 2020.
  * Yu et al. [2020] H. Yu, H. Ma, K. Yang, Y. Zhao, and Y. Jin, “Deepem: Deep neural networks model recovery through em side-channel information leakage,” in _2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)_ , 2020, pp. 209–218.
  * Iyer and Yilmaz [2019] V. V. Iyer and A. E. Yilmaz, “An adaptive acquisition approach to localize electromagnetic information leakage from cryptographic modules,” in _2019 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS)_ , 2019, pp. 1–6.
  * Montminy et al. [2013] D. P. Montminy, R. O. Baldwin, M. A. Temple, and M. E. Oxley, “Differential electromagnetic attacks on a 32-bit microprocessor using software defined radios,” _IEEE Transactions on Information Forensics and Security_ , vol. 8, no. 12, pp. 2101–2114, 2013.
  * Guri et al. [2016] M. Guri, M. Monitz, and Y. Elovici, “Usbee: Air-gap covert-channel via electromagnetic emission from usb,” in _2016 14th Annual Conference on Privacy, Security and Trust (PST)_ , 2016, pp. 264–268.
  * Kocher et al. [1999] P. Kocher, J. Jaffe, and B. Jun, “Differential power analysis,” in _Advances in Cryptology — CRYPTO’ 99_ , M. Wiener, Ed. Springer Berlin Heidelberg, 1999, pp. 388–397.
  * Agrawal et al. [2003] D. Agrawal, J. R. Rao, and P. Rohatgi, “Multi-channel attacks,” in _Cryptographic Hardware and Embedded Systems - CHES 2003_ , C. D. Walter, Ç. K. Koç, and C. Paar, Eds. Springer Berlin Heidelberg, 2003, pp. 2–16.
  * Cheddad et al. [2010] A. Cheddad, J. Condell, K. Curran, and P. Mc Kevitt, “Digital image steganography: Survey and analysis of current methods,” _Signal Processing_ , vol. 90, no. 3, pp. 727–752, 2010.
  * Yang and Sample [2017] C. J. Yang and A. P. Sample, “Em-comm: Touch-based communication via modulated electromagnetic emissions,” _Proc. ACM Interact. Mob. Wearable Ubiquitous Technol._ , vol. 1, no. 3, Sep. 2017. [Online]. Available: https://doi.org/10.1145/3130984
  * Ahmad et al. [2013] M. S. Ahmad, N. E. Musa, R. Nadarajah, R. Hassan, and N. E. Othman, “Comparison between android and ios operating system in terms of security,” in _2013 8th International Conference on Information Technology in Asia (CITA)_ , 2013, pp. 1–4.
  * Aboulkassimi et al. [2011] D. Aboulkassimi, M. Agoyan, L. Freund, J. Fournier, B. Robisson, and A. Tria, “Electromagnetic analysis (ema) of software aes on java mobile phones,” in _2011 IEEE International Workshop on Information Forensics and Security_ , 2011, pp. 1–6.
  * Balasch et al. [2015] J. Balasch, B. Gierlichs, O. Reparaz, and I. Verbauwhede, “Dpa, bitslicing and masking at 1 ghz,” in _International Workshop on Cryptographic Hardware and Embedded Systems_. Springer, 2015, pp. 599–619.
  * Könighofer [2008] R. Könighofer, “A fast and cache-timing resistant implementation of the aes,” in _Topics in Cryptology – CT-RSA 2008_ , T. Malkin, Ed. Springer Berlin Heidelberg, 2008, pp. 187–202.
  * Belgarric et al. [2016] P. Belgarric, P.-A. Fouque, G. Macario-Rat, and M. Tibouchi, “Side-channel analysis of weierstrass and koblitz curve ecdsa on android smartphones,” in _Cryptographers’ Track at the RSA Conference_. Springer, 2016, pp. 236–252.
  * Bukasa et al. [2018] S. K. Bukasa, R. Lashermes, H. Le Bouder, J.-L. Lanet, and A. Legay, “How trustzone could be bypassed: Side-channel attacks on a modern system-on-chip,” in _Information Security Theory and Practice_. Cham: Springer International Publishing, 2018, pp. 93–109.
  * Hu, Xiao-yang et al. [2018] Hu, Xiao-yang, Chen, Kai-yan, Zhang, Yang, Guo, Dong-xin, and Wei, Yan-hai, “Research on Electromagnetic Side-channel Signal Extraction for Mobile Device PCM-9589F Multi-COM,” _MATEC Web Conference_ , vol. 232, p. 04022, 2018. [Online]. Available: https://www.matec-conferences.org/articles/matecconf/abs/2018/91/matecconf_eitce2018_04022/matecconf_eitce2018_04022.html
  * Lin et al. [2017] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications,” _IEEE Internet of Things Journal_ , vol. 4, no. 5, pp. 1125–1142, 2017.
  * Boozer et al. [2021] A. A. Boozer, A. John, and T. Mukherjee, “Internet of Things Software and Hardware Architectures and Their Impacts on Forensic Investigations: Current Approaches and Challenges,” _Journal of Digital Forensics, Security and Law_ , vol. 16, no. 4, 2021. [Online]. Available: https://commons.erau.edu/jdfsl/vol16/iss2/4/
  * Balogh et al. [2021] S. Balogh, O. Gallo, R. Ploszek, P. Špaček, and P. Zajac, “Iot security challenges: Cloud and blockchain, postquantum cryptography, and evolutionary techniques,” _Electronics_ , vol. 10, no. 21, 2021. [Online]. Available: https://www.mdpi.com/2079-9292/10/21/2647
  * Tirumaladass et al. [2020] V. Tirumaladass, S. Axelsson, M. Dougherty, M. A. Rasool, and M. H. Eldefrawy, “Deep learning-based electromagnetic side-channel analysis for the investigation of iot devices,” in _2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)_ , 2020, pp. 150–156.
  * Sayakkara et al. [2020b] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “EMvidence: A Framework for Digital Evidence Acquisition from IoT Devices through Electromagnetic Side-Channel Analysis,” _Forensic Science International: Digital Investigation_ , vol. 32, p. 300907, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666281720300020
  * Levina et al. [2021] A. Levina, V. Varyukhin, D. Kaplun, A. Zamansky, and D. van der Linden, “A case study exploring side-channel attacks on pet wearables.” _IAENG International Journal of Computer Science_ , vol. 48, no. 4, 2021.
  * Lewis [2021] J. Lewis, “Jagger lewis,” https://www.jagger-lewis.com/en-en/home, 2021, accessed: November, 2022.
  * Dinu and Kizhvatov [2018] D. Dinu and I. Kizhvatov, “Em analysis in the iot context: Lessons learned from an attack on thread,” _IACR Transactions on Cryptographic Hardware and Embedded Systems_ , vol. 2018, no. 1, p. 73–97, Feb. 2018. [Online]. Available: https://tches.iacr.org/index.php/TCHES/article/view/833
  * Durvaux and Durvaux [2020] F. Durvaux and M. Durvaux, “Sca-pitaya: A practical and affordable side-channel attack setup for power leakage–based evaluations,” _Digital Threats_ , vol. 1, no. 1, Mar. 2020. [Online]. Available: https://doi.org/10.1145/3371393
  * Agosta et al. [2018] G. Agosta, A. Barenghi, G. Pelosi, and M. Scandale, “Reactive side-channel countermeasures: Applicability and quantitative security evaluation,” _Microprocessors and Microsystems_ , vol. 62, pp. 50–60, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0141933118301790
  * Jevtic et al. [2021] R. Jevtic, M. Ylitolva, C. Calonge, M. Ojanen, T. Santti, and L. Koskinen, “Em side-channel countermeasure for switched-capacitor dc–dc converters based on amplitude modulation,” _IEEE Transactions on Very Large Scale Integration (VLSI) Systems_ , vol. 29, no. 6, pp. 1061–1072, 2021.
  * Singh et al. [2019a] A. Singh, M. Kar, S. Mathew, A. Rajan, V. De, and S. Mukhopadhyay, “25.3 a 128b aes engine with higher resistance to power and electromagnetic side-channel attacks enabled by a security-aware integrated all-digital low-dropout regulator,” in _2019 IEEE International Solid-State Circuits Conference - (ISSCC)_ , 2019, pp. 404–406.
  * Das et al. [2021a] D. Das, S. Ghosh, A. Raychowdhury, and S. Sen, “Em/power side-channel attack: White-box modeling and signature attenuation countermeasures,” _IEEE Design & Test_, vol. 38, no. 3, pp. 67–75, 2021.
  * Nassar et al. [2012] M. Nassar, Y. Souissi, S. Guilley, and J.-L. Danger, “Rsm: A small and fast countermeasure for aes, secure against 1st and 2nd-order zero-offset scas,” in _2012 Design, Automation & Test in Europe Conference & Exhibition (DATE)_, 2012, pp. 1173–1178.
  * Chong et al. [2021a] K.-S. Chong, J.-S. Ng, J. Chen, N. K. Z. Lwin, N. A. Kyaw, W.-G. Ho, J. Chang, and B.-H. Gwee, “Dual-hiding side-channel-attack resistant fpga-based asynchronous-logic aes: Design, countermeasures and evaluation,” _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ , vol. 11, no. 2, pp. 343–356, 2021.
  * Shan et al. [2014] W. Shan, L. Shi, X. Fu, X. Zhang, C. Tian, Z. Xu, J. Yang, and J. Li, “A side-channel analysis resistant reconfigurable cryptographic coprocessor supporting multiple block cipher algorithms,” in _Proceedings of the 51st Annual Design Automation Conference_ , ser. DAC ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 1–6. [Online]. Available: https://doi.org/10.1145/2593069.2593077
  * Khan et al. [2017] A. W. Khan, T. Wanchoo, G. Mumcu, and S. Köse, “Implications of distributed on-chip power delivery on em side-channel attacks,” in _2017 IEEE International Conference on Computer Design (ICCD)_ , 2017, pp. 329–336.
  * Judy et al. [2022] R. Judy, A. Smith, L. Wallace, and X. Chen, “Electromagnetic waveform characterization for side-channel attacks on aes encryption,” in _2022 IEEE Physical Assurance and Inspection of Electronics (PAINE)_ , 2022, pp. 1–7.
  * Gao et al. [2023] Y. Gao, Q. Zhang, H. Ma, J. He, and Y. Zhao, “Eo-shield: A multi-function protection scheme against side channel and focused ion beam attacks,” in _Proceedings of the 28th Asia and South Pacific Design Automation Conference_ , ser. ASPDAC ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 670–675. [Online]. Available: https://doi.org/10.1145/3566097.3567924
  * He et al. [2011] W. He, C. Pizarro, E. de la Torre, J. Portilla, and T. Riesgo, “SCA Security Verification on Wireless Sensor Network Node,” _VLSI Circuits and Systems V_ , vol. 8067, pp. 298–312, 2011. [Online]. Available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8067/80670W/SCA-security-verification-on-wireless-sensor-network-node/10.1117/12.887537.short
  * Frieslaar and Irwin [2017] I. Frieslaar and B. Irwin, “Investigating the electromagnetic side channel leakage from a raspberry pi,” in _2017 Information Security for South Africa (ISSA)_ , 2017, pp. 24–31.
  * Pammu et al. [2016b] A. A. Pammu, K.-S. Chong, and B.-H. Gwee, “Highly secured arithmetic hiding based s-box on aes-128 implementation,” in _2016 International Symposium on Integrated Circuits (ISIC)_ , 2016, pp. 1–4.
  * Zhou and Kong [2019] W.-h. Zhou and F.-t. Kong, “Electromagnetic side channel attack against embedded encryption chips,” in _2019 IEEE 19th International Conference on Communication Technology (ICCT)_ , 2019, pp. 140–144.
  * Carlier et al. [2005] V. Carlier, H. Chabanne, E. Dottax, and H. Pelletier, “Generalizing square attack using side-channels of an aes implementation on an fpga,” in _International Conference on Field Programmable Logic and Applications, 2005._ IEEE, 2005, pp. 433–437.
  * Singh et al. [2019b] A. Singh, M. Kar, S. K. Mathew, A. Rajan, V. De, and S. Mukhopadhyay, “Improved power/em side-channel attack resistance of 128-bit aes engines with random fast voltage dithering,” _IEEE Journal of Solid-State Circuits_ , vol. 54, no. 2, pp. 569–583, 2019.
  * Chong et al. [2021b] K.-S. Chong, J.-S. Ng, J. Chen, N. K. Z. Lwin, N. A. Kyaw, W.-G. Ho, J. Chang, and B.-H. Gwee, “Dual-hiding side-channel-attack resistant fpga-based asynchronous-logic aes: Design, countermeasures and evaluation,” _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ , vol. 11, no. 2, pp. 343–356, 2021.
  * Chawla et al. [2020] N. Chawla, A. Singh, H. Kumar, M. Kar, and S. Mukhopadhyay, “Securing IoT Devices Using Dynamic Power Management: Machine Learning Approach,” _IEEE Internet of Things Journal_ , vol. 8, no. 22, pp. 16 379–16 394, 2020.
  * Buchanan and Woodward [2017] W. Buchanan and A. Woodward, “Will quantum computers be the end of public key encryption?” _Journal of Cyber Security Technology_ , vol. 1, no. 1, pp. 1–22, 2017.
  * Mavroeidis et al. [2018] V. Mavroeidis, K. Vishi, M. D. Zych, and A. Jøsang, “The impact of quantum computing on present cryptography,” _International Journal of Advanced Computer Science and Applications_ , vol. 9, no. 3, 2018.
  * Fernández-Caramès and Fraga-Lamas [2020] T. M. Fernández-Caramès and P. Fraga-Lamas, “Towards post-quantum blockchain: A review on blockchain cryptography resistant to quantum computing attacks,” _IEEE Access_ , vol. 8, pp. 21 091–21 116, 2020.
  * Sendrier [2017] N. Sendrier, “Code-based cryptography: State of the art and perspectives,” _IEEE Security & Privacy_, vol. 15, no. 4, pp. 44–50, 2017.
  * D’Anvers et al. [2019] J.-P. D’Anvers, M. Tiepelt, F. Vercauteren, and I. Verbauwhede, “Timing attacks on error correcting codes in post-quantum schemes,” in _Proceedings of ACM Workshop on Theory of Implementation Security Workshop_ , ser. TIS’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2–9. [Online]. Available: https://doi.org/10.1145/3338467.3358948
  * Das et al. [2021b] D. Das, J. Danial, A. Golder, N. Modak, S. Maity, B. Chatterjee, D.-H. Seo, M. Chang, A. L. Varna, H. K. Krishnamurthy, S. Mathew, S. Ghosh, A. Raychowdhury, and S. Sen, “Em and power sca-resilient aes-256 through ¿350× current-domain signature attenuation and local lower metal routing,” _IEEE Journal of Solid-State Circuits_ , vol. 56, no. 1, pp. 136–150, 2021.
  * Iyer and Yılmaz [2022] V. V. Iyer and A. E. Yılmaz, “An anova method to rapidly assess information leakage near cryptographic modules,” _IEEE Transactions on Electromagnetic Compatibility_ , vol. 64, no. 4, pp. 915–929, 2022.
  * Danial et al. [2021] J. Danial, D. Das, A. Golder, S. Ghosh, A. Raychowdhury, and S. Sen, “Em-x-dl: Efficient cross-device deep learning side-channel attack with noisy em signatures,” _Journal on Emerging Technologies in Computing Systems_ , vol. 18, no. 1, Sep. 2021. [Online]. Available: https://doi.org/10.1145/3465380
  * Mukhtar and Kong [2018] N. Mukhtar and Y. Kong, “Hyper-parameter optimization for machine-learning based electromagnetic side-channel analysis,” in _2018 26th International Conference on Systems Engineering (ICSEng)_ , 2018, pp. 1–7.
  * Ghosh et al. [2022] A. Ghosh, M. Nath, D. Das, S. Ghosh, and S. Sen, “Electromagnetic analysis of integrated on-chip sensing loop for side-channel and fault-injection attack detection,” _IEEE Microwave and Wireless Components Letters_ , vol. 32, no. 6, pp. 784–787, 2022.
  * Veyrat-Charvillon and Standaert [2010] N. Veyrat-Charvillon and F.-X. Standaert, “Adaptive chosen-message side-channel attacks,” in _Proceedings of the 8th International Conference on Applied Cryptography and Network Security_ , ser. ACNS’10. Berlin, Heidelberg: Springer-Verlag, 2010, p. 186–199.
  * Choudary and Kuhn [2018] M. O. Choudary and M. G. Kuhn, “Efficient, portable template attacks,” _IEEE Transactions on Information Forensics and Security_ , vol. 13, no. 2, pp. 490–501, 2018.
  * Tunstall [2017] M. Tunstall, _Smart Card Security_. Cham: Springer International Publishing, 2017, pp. 217–251. [Online]. Available: https://doi.org/10.1007/978-3-319-50500-8_9
  * Nomata et al. [2016] Y. Nomata, M. Matsubayashi, K. Sawada, and A. Satoh, “Comparison of side-channel attack on cryptographic cirucits between old and new technology fpgas,” in _2016 IEEE 5th Global Conference on Consumer Electronics_ , 2016, pp. 1–4.
  * He et al. [2020] J. He, H. Ma, X. Guo, Y. Zhao, and Y. Jin, “Design for em side-channel security through quantitative assessment of rtl implementations,” in _2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)_ , 2020, pp. 62–67.
  * Kar et al. [2018a] M. Kar, A. Singh, S. K. Mathew, A. Rajan, V. De, and S. Mukhopadhyay, “Reducing power side-channel information leakage of aes engines using fully integrated inductive voltage regulator,” _IEEE Journal of Solid-State Circuits_ , vol. 53, no. 8, pp. 2399–2414, 2018.
  * Poggi et al. [2022] D. Poggi, P. Maurine, T. Ordas, A. Sarafianos, and J. Raoult, “EM Emission Modeling for Secure IC Design,” in _2021 13th International Workshop on the Electromagnetic Compatibility of Integrated Circuits (EMC Compo)_ , 2022, pp. 39–44.
  * De Mulder et al. [2006] E. De Mulder, S. B. Ors, B. Preneel, and I. Verbauwhede, “Differential electromagnetic attack on an fpga implementation of elliptic curve cryptosystems,” in _2006 World Automation Congress_ , 2006, pp. 1–6.
  * Soares et al. [2010] R. I. Soares, N. L. V. Calazans, V. Lomné, A. Dehbaoui, P. Maurine, and L. Torres, “A gals pipeline des architecture to increase robustness against dpa and dema attacks,” ser. SBCCI ’10. New York, NY, USA: Association for Computing Machinery, 2010, p. 115–120. [Online]. Available: https://doi.org/10.1145/1854153.1854183
  * Shan et al. [2015] W. Shan, X. Fu, and Z. Xu, “A secure reconfigurable crypto ic with countermeasures against spa, dpa, and ema,” _IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems_ , vol. 34, no. 7, pp. 1201–1205, 2015.
  * Maistri et al. [2013] P. Maistri, S. Tiran, P. Maurine, I. Koren, and R. Leveugle, “Countermeasures against em analysis for a secured fpga-based aes implementation,” in _2013 International Conference on Reconfigurable Computing and FPGAs (ReConFig)_ , 2013, pp. 1–6.
  * Das et al. [2019] D. Das, M. Nath, B. Chatterjee, S. Ghosh, and S. Sen, “Stellar: A generic em side-channel attack protection through ground-up root-cause analysis,” in _2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)_ , 2019, pp. 11–20.
  * Dyrkolbotn and Snekkenes [2009] G. O. Dyrkolbotn and E. Snekkenes, “Modified template attack: Detecting address bus signals of equal hamming weight,” in _The Norwegian Information Security Conference (NISK)_ , 2009, pp. 43–56.
  * Picek et al. [2018] S. Picek, A. Heuser, L. Wu, C. Alippi, and F. Regazzoni, “When Theory meets Practice: A Framework for Robust Profiled Side-channel Analysis,” _Cryptology ePrint Archive_ , 2018. [Online]. Available: https://eprint.iacr.org/2018/1123
  * Callan et al. [2016] R. Callan, F. Behrang, A. Zajic, M. Prvulovic, and A. Orso, “Zero-overhead profiling via em emanations,” in _Proceedings of the 25th International Symposium on Software Testing and Analysis_ , ser. ISSTA 2016. New York, NY, USA: Association for Computing Machinery, 2016, p. 401–412. [Online]. Available: https://doi.org/10.1145/2931037.2931065
  * Jiang and Pavlidis [2021] M. Jiang and V. F. Pavlidis, “A probe placement method for efficient electromagnetic attacks,” in _SMACD / PRIME 2021; International Conference on SMACD and 16th Conference on PRIME_ , 2021, pp. 1–4.
  * Pammu et al. [2019] A. A. Pammu, W.-G. Ho, N. K. Z. Lwin, K.-S. Chong, and B.-H. Gwee, “A high throughput and secure authentication-encryption aes-ccm algorithm on asynchronous multicore processor,” _IEEE Transactions on Information Forensics and Security_ , vol. 14, no. 4, pp. 1023–1036, 2019.
  * Kar et al. [2018b] M. Kar, A. Singh, S. Ghosh, S. Mathew, A. Rajan, V. De, R. Beyah, and S. Mukhopadhyay, “Blindsight: Blinding EM side-channel leakage using built-in fully integrated inductive voltage regulator,” _arXiv Preprint_ , 2018. [Online]. Available: https://arxiv.org/abs/1802.09096
  * Liu et al. [2012] B. Liu, H. Feng, Z. Yuan, and Y. Gao, “Learning to attack from electromagnetic emanation,” in _2012 6th Asia-Pacific Conference on Environmental Electromagnetics (CEEM)_ , 2012, pp. 202–205.
  * Le et al. [2007] T.-H. Le, J. Clediere, C. Serviere, and J.-L. Lacoume, “Noise reduction in side channel attack using fourth-order cumulant,” _IEEE Transactions on Information Forensics and Security_ , vol. 2, no. 4, pp. 710–720, 2007.
  * Schlumberger et al. [2021] J. Schlumberger, S. Wildermann, and J. Teich, “Corsica: A framework for conducting real-world side-channel analysis,” in _2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS)_ , 2021, pp. 1–5.
  * Ravi et al. [2019] P. Ravi, B. Jungk, and S. Bhasin, “Single trace electromagnetic side-channel attacks on fpga implementation of elliptic curve cryptography,” in _2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC)_ , 2019, pp. 1–4.
  * Nomikos et al. [2020] K. Nomikos, A. Papadimitriou, G. Stergiopoulos, D. Koutras, M. Psarakis, and P. Kotzanikolaou, “On a security-oriented design framework for medical iot devices: The hardware security perspective,” in _2020 23rd Euromicro Conference on Digital System Design (DSD)_ , 2020, pp. 301–308.
  * Cammarota et al. [2018] R. Cammarota, I. Banerjee, and O. Rosenberg, “Machine learning IP protection,” _2018 IEEE/ACM International_ , 2018. [Online]. Available: https://dl.acm.org/doi/abs/10.1145/3240765.3270589
  * Sayakkara et al. [2020c] A. Sayakkara, N.-A. Le-Khac, and M. Scanlon, “Facilitating Electromagnetic Side-Channel Analysis for IoT Investigation: Evaluating the EMvidence Framework,” _Forensic Science International: Digital Investigation_ , vol. 33, p. 301003, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666281720302523
  * Seçkiner and Köse [2021] S. Seçkiner and S. Köse, “Preprocessing of the physical leakage information to combine side-channel distinguishers,” _IEEE Transactions on Very Large Scale Integration (VLSI) Systems_ , vol. 29, no. 12, pp. 2052–2063, 2021.
  * Kang et al. [2009] Y. S. Kang, D. H. Choi, B. H. Chung, H. S. Cho, and D.-G. Han, “Efficient key detection method in the correlation electromagnetic analysis using peak selection algorithm,” _Journal of Communications and Networks_ , vol. 11, no. 6, pp. 556–563, 2009.
  * Gunathilake et al. [2021] N. A. Gunathilake, A. Al-Dubai, W. J. Buchanan, and O. Lo, “Electromagnetic analysis of an ultra-lightweight cipher: Present,” in _10th International Conference on Cryptography and Information Security (CRYPIS 2021)_ , vol. 11, 2021, pp. 185–205.
  * Chawla et al. [2019] N. Chawla, A. Singh, M. Kar, and S. Mukhopadhyay, “Application inference using machine learning based side channel analysis,” pp. 1–8, 2019.
  * Chawla et al. [2021] N. Chawla, H. Kumar, and S. Mukhopadhyay, “Machine learning in wavelet domain for electromagnetic emission based malware analysis,” _IEEE Transactions on Information Forensics and Security_ , vol. 16, pp. 3426–3441, 2021.
  * Lorenzo et al. [2019] F. Lorenzo, J. T. McDonald, T. R. Andel, W. B. Glisson, and S. Russ, “Evaluating side channel resilience in iphone 5c unlock scenarios,” in _2019 SoutheastCon_ , 2019, pp. 1–7.
  * Leignac et al. [2019] P. Leignac, O. Potin, J.-B. Rigaud, J.-M. Dutertre, and S. Pontié, “Comparison of side-channel leakage on rich and trusted execution environments,” in _Proceedings of the Sixth Workshop on Cryptography and Security in Computing Systems_ , ser. CS2 ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 19–22. [Online]. Available: https://doi.org/10.1145/3304080.3304084
  * Tuveri et al. [2018] N. Tuveri, S. u. Hassan, C. P. Garcia, and B. B. Brumley, “Side-channel analysis of sm2: A late-stage featurization case study,” in _Proceedings of the 34th Annual Computer Security Applications Conference_ , ser. ACSAC ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 147–160. [Online]. Available: https://doi.org/10.1145/3274694.3274725
  * Khan et al. [2018] H. A. Khan, M. Alam, A. Zajic, and M. Prvulovic, “Detailed Tracking of Program Control Flow Using Analog Side-channel Signals: A Promise for IoT Malware Detection and a Threat for Many Cryptographic Implementations,” in _Cyber Sensing 2018_ , I. V. Ternovskiy and P. Chin, Eds., vol. 10630, International Society for Optics and Photonics. SPIE, 2018, p. 1063005. [Online]. Available: https://doi.org/10.1117/12.2304382
  * Wakabayashi et al. [2018] S. Wakabayashi, S. Maruyama, T. Mori, S. Goto, M. Kinugawa, and Y.-I. Hayashi, “A feasibility study of radio-frequency retroreflector attack,” in _Proceedings of the 12th USENIX Conference on Offensive Technologies_ , ser. WOOT’18. USA: USENIX Association, 2018, p. 4.
  * García et al. [2020] C. P. García, S. Ul Hassan, N. Tuveri, I. Gridin, A. C. Aldaya, and B. B. Brumley, “Certified side channels,” in _Proceedings of the 29th USENIX Conference on Security Symposium_ , ser. SEC’20. USA: USENIX Association, 2020.

##  Appendix A Classification of the Systematically Reviewed Literature

TABLE I: Listing and Classification of Included Literature (Grouped by
Targeted Device Type) Legend
---
| High Relevancy/Applicability
| Medium Relevancy/Applicability
[regular] | Low Relevancy/Applicability
- | No Relevancy/Applicability
| Computer
* | Mobile Device
| Network Device
| Internet of Things Devices
| Integrated Circuit/FPGA/Microcontroller/Microprocessor
| Multiple Devices
| Oscilloscope
| Software Defined Radio
✓ | Yes
✗ | No
Reference |  AES Encryption |  DES Encryption |  ECC Encryption |  EEA Encryption |  RSA Encryption |  Other Encryption |  Instrumented? |  Device Type |  EM Capture |  Machine Learning |  Deep Learning |  Key Recovery
---|---|---|---|---|---|---|---|---|---|---|---|---
Targeted Device: IC/FPGA/Microcontroller/Microprocessor |
Das et al. [233] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Iyer et al. [140] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Iyer and Yilmaz [187] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Iyer and Yılmaz [234] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Hell and Westman [124] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Yu et al. [117] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
Duan et al. [182] |  |  [regular] |  - |  - |  - |  - |  ✓ |  |  |  |  - |  [regular]
Danial et al. [235] |  |  - |  - |  - |  |  - |  ✓ |  |  |  - |  |
Mukhtar and Kong [236] |  |  [regular] |  - |  - |  - |  - |  ✓ |  |  |  |  - |
Ghosh et al. [237] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  |  - |  [regular]
Veyrat-Charvillon and Standaert [238] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Choudary and Kuhn [239] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Naija et al. [164] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Kasper et al. [162] |  |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Tunstall [240] |  [regular] |  |  - |  - |  |  - |  ✓ |  |  |  - |  - |
Burnside et al. [97] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Nomata et al. [241] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Bu et al. [145] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
He et al. [242] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Sauvage et al. [98] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Simon and Patel [156] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Zhou and Kong [223] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Pammu et al. [222] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Kar et al. [243] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Danial et al. [10] |  |  |  - |  - |  |  - |  ✓ |  |  |  - |  - |
Lellis et al. [99] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Montminy et al. [188] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Ravi et al. [129] |  - |  - |  |  - |  - |  - |  ✓ |  |  |  - |  - |
Poggi et al. [244] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Wang et al. [114] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
De Mulder et al. [245] |  - |  - |  |  - |  - |  - |  ✓ |  |  |  - |  - |
Soares et al. [246] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Shan et al. [247] |  |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Maistri et al. [248] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Yoshida et al. [133] |  - |  - |  - |  - |  - |  [regular] |  ✗ |  |  |  - |  - |
Das et al. [249] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Yu et al. [186] |  - |  - |  - |  - |  - |  [regular] |  ✓ |  |  |  - |  |  [regular]
Dyrkolbotn and Snekkenes [250] |  - |  - |  - |  - |  - |  [regular] |  ✓ |  |  |  |  - |  [regular]
Abdellatif [138] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Picek et al. [251] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  |  |
Callan et al. [252] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  |  - |  [regular]
Jiang and Pavlidis [253] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Pammu et al. [254] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Won and Bhasin [47] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
Chusseau et al. [125] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Ueno et al. [48] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
Won and Bhasin [47] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
Kar et al. [255] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Liu et al. [256] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  |  - |
Le et al. [257] |  - |  |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Schlumberger et al. [258] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Gandolfi et al. [9] |  - |  |  - |  - |  |  - |  ✓ |  |  |  - |  - |  [regular]
Dehbaoui et al. [127] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Nozaki et al. [154] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Hori et al. [183] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Ravi et al. [259] |  - |  - |  |  - |  [regular] |  - |  ✓ |  |  |  - |  - |
Iyer and Yilmaz [96] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Targeted Device: IoT Devices |
Mukhtar et al. [100] |  [regular] |  - |  |  - |  - |  - |  ✓ |  |  |  - |  |
Das and Sen [184] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Nomikos et al. [260] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Camurati et al. [115] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Le Bouder et al. [155] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Naija et al. [166] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Cammarota et al. [261] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Tirumaladass et al. [204] |  |  |  - |  - |  [regular] |  - |  ✓ |  |  |  |  |  [regular]
Sayakkara et al. [262] |  - |  - |  |  - |  - |  - |  ✓ |  |  |  |  |
Sayakkara et al. [4] |  |  |  - |  - |  - |  - |  ✓ |  |  |  |  - |
Durvaux and Durvaux [209] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Frieslaar and Irwin [221] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
He et al. [220] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Seçkiner and Köse [263] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  [regular] |  [regular] |
Kang et al. [264] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Gunathilake et al. [265] |  |  [regular] |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Gunathilake et al. [123] |  [regular] |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Pammu et al. [107] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Kim et al. [175] |  [regular] |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Frieslaar and Irwin [109] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Sayakkara et al. [11] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  [regular] |  [regular] |  [regular]
Dinu and Kizhvatov [208] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Bukasa et al. [199] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Jap et al. [118] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  |  - |
Lim et al. [122] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Levina et al. [206] |  - |  - |  - |  - |  - |  [regular] |  ✓ |  |  |  - |  - |
Chawla et al. [266] |  - |  - |  - |  - |  - |  [regular] |  ✓ |  |  |  |  - |  [regular]
Hutter et al. [177] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Targeted Device: Mobile Devices |
Haas and Aysu [94] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  - |
Genkin et al. [102] |  - |  - |  |  - |  [regular] |  - |  ✓ |  * |  |  - |  - |
Vasselle et al. [93] |  |  - |  [regular] |  - |  [regular] |  - |  ✓ |  * |  |  - |  - |
Xiao et al. [113] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  |
Chawla et al. [267] |  - |  - |  - |  - |  - |  |  ✓ |  * |  |  |  [regular] |  [regular]
Lorenzo et al. [268] |  - |  - |  [regular] |  - |  - |  - |  ✓ |  * |  |  - |  - |  [regular]
Hu, Xiao-yang et al. [200] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  - |  [regular]
Leignac et al. [269] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  - |
Pan et al. [119] |  - |  - |  - |  - |  - |  [regular] |  ✓ |  * |  |  |  |
Goller and Sigl [103] |  - |  - |  - |  - |  |  - |  ✓ |  * |  |  - |  - |
Nakano et al. [104] |  - |  - |  |  - |  |  - |  ✓ |  * |  |  - |  - |
Kenworthy and Rohatgi [105] |  |  - |  |  - |  |  - |  ✓ |  * |  |  - |  - |
Balasch et al. [196] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  - |
Belgarric et al. [198] |  - |  - |  |  - |  - |  - |  ✓ |  * |  |  - |  - |
Lisovets et al. [49] |  |  - |  - |  - |  - |  - |  ✓ |  * |  |  - |  - |
Targeted Device: Computer Devices |
Yu and Chen [80] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  |
Kumar et al. [92] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Genkin et al. [101] |  |  - |  |  - |  - |  - |  ✓ |  |  |  - |  - |
Tuveri et al. [270] |  [regular] |  - |  |  [regular] |  |  - |  ✓ |  |  |  - |  - |
Nakamura et al. [126] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Yilmaz et al. [185] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |  [regular]
Genkin et al. [106] |  - |  - |  - |  - |  |  - |  ✓ |  |  |  - |  - |
Ding et al. [108] |  |  - |  - |  - |  - |  - |  ✓ |  |  |  - |  - |
Khan et al. [271] |  - |  - |  - |  - |  |  - |  ✓ |  |  |  |  - |
Wakabayashi et al. [272] |  - |  - |  - |  - |  - |  |  ✓ |  |  |  - |  - |
Zhou and Standaert [144] |  - |  - |  |  - |  - |  - |  ✓ |  |  |  - |  |
Targeted Device: Multiple Devices |
Plos et al. [163] |  |  - |  [regular] |  - |  - |  - |  ✓ |  |  |  - |  - |  [regular]
Alam et al. [110] |  - |  - |  |  - |  - |  - |  ✓ |  |  |  - |  - |
Targeted Device: Network Devices |
García et al. [273] |  - |  - |  |  [regular] |  [regular] |  - |  ✓ |  |  |  - |  - |