# Reconfigurable Intelligent Surface for Physical Layer Security in 6G-IoT:
Designs, Issues, and Advances

Waqas Khalid, M. Atif Ur Rehman, Trinh Van Chien, Zeeshan Kaleem, Howon Lee,
Heejung Yu Waqas Khalid is with the Institute of Industrial Technology, Korea
University, Sejong 30019, Korea<EMAIL_ADDRESS>korea.ac.kr}). M.
Atif Ur Rehman is with the Manchester Metropolitan University, Manchester, UK
(email:m.atif.ur.rehman@mmu.ac.uk). Trinh Van Chien is with the Hanoi
University of Science and Technology, Hanoi, Vietnam
(email:chientv@soict.hust.edu.vn). Zeeshan Kaleem is with the COMSATS
University Islamabad, Wah Campus, Pakistan<EMAIL_ADDRESS>Howon Lee (Corresponding author) is with the School of Electronic and
Electrical Engineering and IITC, Hankyong National University, Anseong 17579,
South Korea (e-mail: hwlee@hknu.ac.kr). Heejung Yu (Corresponding author) is
with the Department of Electronics and Information Engineering, Korea
University, Sejong 30019, Korea (email:heejungyu@korea.ac.kr).

###### Abstract

Sixth-generation (6G) networks pose substantial security risks because
confidential information is transmitted over wireless channels with a
broadcast nature, and various attack vectors emerge. Physical layer security
(PLS) exploits the dynamic characteristics of wireless environments to provide
secure communications, while reconfigurable intelligent surfaces (RISs) can
facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a
lightweight security solution can be designed for low-end Internet of Things
(IoT) devices, depending on the design scenario and communication objective.
This article discusses RIS-aided PLS designs for 6G-IoT networks against
eavesdropping and jamming attacks. The theoretical background and literature
review of RIS-aided PLS are discussed, and design solutions related to
resource allocation, beamforming, artificial noise, and cooperative
communication are presented. We provide simulation results to show the
effectiveness of RIS in terms of PLS. In addition, we examine the research
issues and possible solutions for RIS modeling, channel modeling and
estimation, optimization, and machine learning. Finally, we discuss recent
advances, including STAR-RIS and malicious RIS.

###### Index Terms:

6G, Internet of Things (IoT), physical layer security (PLS), reconfigurable
intelligent surface (RIS).

Figure 1: Organization of the paper.

##  I Introduction

Sixth-generation (6G) networks offer innovative use cases, unprecedented
services, and revolutionary applications owing to their high transmission
rates, significantly low latency, and enhanced reliability. The Internet of
Things (IoT) is a recently developed communication paradigm that provides all-
encompassing connectivity solutions in 6G [1]. Nevertheless, 6G-IoT networks
pose severe security risks due to the broadcast nature of wireless channels,
large amounts of confidential and privacy-sensitive data (e.g., online
banking, personal tracking, home control, health monitoring, and industrial
automation), and an exponential increase in attack vectors. A 6G-IoT network
must be hyper-secure from the physical to the application layers. In this
regard, physical layer security (PLS) is an effective method of securing
legitimate transmissions by leveraging the dynamic nature of wireless
communications [2].

Millimeter-wave (mmWave) and sub-terahertz (sub-THz) frequencies offer
considerable advantages despite unique propagation challenges, such as high
sensitivity to blockages and severe penetration loss [1, 2]. As an example,
point-to-point links at these frequencies have significantly higher
transmission losses than those at sub-6 GHz systems. Therefore, future
algorithms and protocols must be designed to mitigate the adverse effects of
an uncontrolled radio environment. A conventional transmission strategy
optimizes a transmitter (Tx) and receiver (Rx) through various approaches,
such as multiple antennas, complex signal processing algorithms, sophisticated
encoding and decoding procedures, and advanced communication protocols.
However, the Tx and Rx design cannot be extremely complex in certain
application scenarios, such as resource-constrained IoT and industrial field
devices.

Table I lists the entities discussed in the existing literature for
controlling the radio environment, including reconfigurable intelligent
surfaces (RISs) [3], passive analog repeaters [4, 5], decode-and-forward (DF)
relays [6], and amplify-and-forward (AF) relays [7]. Particularly, RIS is an
emerging technology that can provide a dynamic, controllable, and programmable
radio environment. Further, different metasurface implementations have been
developed for the full space environment, including simultaneous transmitting
and reflecting-RIS (STAR-RIS) and intelligent omni-surface (IOS) [8]. Owing to
the dependence of the PLS on a richly scattered environment, the dynamic
control of channels achieved by the RIS can significantly improve security in
6G-IoT networks.

TABLE I: Entities that control the radio environment  Entity |  Type |  Advantages |  Disadvantages
---|---|---|---
RIS |  Transparent |  • Full-duplex (FD) operation • Low power consumption • Noise is not created due to the absence of active components (introduces a phase shift) • Simple RF processing |  • Must be controlled by a base station (BS) with an additional control link (wired or wireless) • Discrete phase shift and phase noise in the hardware • Complex channel estimation and reflection pattern design
Passive analog repeater |  Transparent |  • FD operation • Simple and cost-effective design • Not require any control from a BS • Fully passive (only reflection) |  • No flexibility, fixed beams • Reflects the signal without amplification (limited achievable transmission range) • Dependence on signal strength
DF-relay |  Regenerative |  • Supports half-duplex (HD) and FD modes • No noise amplification problem • Signals received can be intentionally analyzed and decoded |  • Higher hardware complexity • High power consumption compared to RIS • Signal processing operations are more complex than RIS
AF-relay |  Transparent |  • Supports both HD and FD modes • Signal is boosted (amplified) and forwarded without further processing |  • Noise amplification • Hardware complexity lower than DF • High power consumption compared to RIS • Signal processing operations are more complex than RIS

Continuing next, the motivation, contribution, and organization of the paper
will be presented as follows:

Motivation: PLS can be used independently or in combination with post-quantum
cryptography for the security of simple 6G-IoT devices [9]. PLS methods may be
ineffective in unfavorable propagation conditions because they rely on noise
and fading variations in wireless channels. Security can be enhanced in 6G-IoT
networks with dynamically controlled channels provided by RIS [10]. For
example, an RIS can completely exploit PLS advantages caused by channel
propagation characteristics, spatial diversity, beamforming, and cooperative
communications. RIS-aided PLS designs can be applied to both 5G and 6G without
distinction [11], however, they require precise configuration, placement of
the RIS, strict hardware requirements, and elements coupling. In addition,
RIS-aided PLS solutions can be selected based on the design scenario and
communication objective, balancing security performance and implementation
complexity [9, 10, 11].

Contribution: Based on the aforementioned motivation, we present a general
framework for designing and implementing PLS solutions using RIS to prevent
eavesdropping and jamming attacks in the 6G-IoT wireless paradigm. The RIS is
introduced in terms of fundamental principles and hardware architecture, and
the PLS is presented in terms of background concepts, leveraging sources, and
security methods. We discuss the theoretical background and literature review
of RIS-aided PLS and present design solutions related to resource allocation,
beamforming, artificial noise (AN), and cooperative communications. Simulation
results are also provided to verify the effectiveness of RIS in terms of the
PLS performance measures, e.g., secrecy rate and secrecy outage probability,
under various network configurations. In addition, we examine the research
issues and possible solutions for RIS modeling, channel modeling, channel
estimation, optimization, and machine learning (ML), and discuss recent
advances, such as STAR-RIS and malicious RIS.

Organization: Fig. 1 illustrates the organization of this article. Section II
introduces RIS and PLS. Section III presents the background, literature
review, and design solutions of the RIS-aided PLS, along with simulation
results. We discuss research issues and possible solutions, and recent
advances, respectively, in Sections IV and V. Finally, Section VI concludes
the article.

##  II Overview: RIS and PLS

In this section, an RIS is introduced in terms of fundamental principles and
hardware architecture, and PLS is discussed in terms of background concepts,
leveraging sources for security, and security requirements and methods.

###  II-A RIS: Fundamental Principle and Hardware Architecture

RIS is a hardware transmission technology that reconfigures wireless channels
and establishes a smart radio environment. As shown in Fig. 2, the RIS
configuration comprises three layers and a controller with the following
details [12]:

  * •

The outer layer has flexible, discrete, sub-wavelength-sized elements. An
effective and scalable method of modifying tunable elements and controlling
electromagnetic (EM) reflection is to use a diode array, such as a positive-
intrinsic-negative (PIN) or varactor diode array. A voltage setting can alter
the state of the diode, thereby controlling the tunable elements.

  * •

A copper backplane is placed in the middle layer to prevent signal leakage.

  * •

In the inner layer, a control circuit board adjusts the reflection
coefficients (amplitude and phase). It connects the RIS controller to the
reflecting elements, i.e., it receives and transmits voltage signals.

  * •

An RIS controller adjusts the phase of the incident signal based on commands
from the central controller. It is implemented using a field-programmable gate
array (FPGA), which acts as a gateway between other network components (e.g.,
BSs and user terminals).

By manipulating the incident EM waves, RIS can produce the desired scattering
and reflection patterns while providing solutions to radio frequency (RF)
impairments and signal distortions. Through controlled scattering and multi-
path components, RIS extends transmission coverage under harsh propagation
conditions, such as blockage and shadowing, and ensures wireless connectivity
over multiple concurrent links [13]. Accordingly, better coverage and
reliability can be achieved without sophisticated signal processing or RF
operations. The radio environment is not considered an uncontrollable and
random entity because it is part of the design parameter in an optimization
space. Joint optimization of the Tx, Rx, and wireless channels can
significantly improve performance metrics, including rate, latency,
reliability, privacy, energy efficiency, and massive connectivity. Through
intelligent placement and effective beamforming designs, the RISs can enable
advanced wireless functions, such as a signal boost in mmWave and sub-THz
bands, power consumption reduction, increased throughput, interference
suppression, and reliable and secure reception. Hence, 6G-IoT networks can
maximize their potential [12, 13].

Figure 2: Schematic of an RIS, where a 6G controller configures elements to
control the reflection depending on PLS requirements.

###  II-B PLS: Security Concepts and Methods

####  II-B1 Background Concepts

Cryptography and PLS are promising solutions to secure wireless transmissions.
Cryptography provides security at the higher layers using classical
encryption-based algorithms and protocols, and PLS manipulates the intrinsic
characteristics of the wireless channels [14]. Cryptography-based solutions
have the following key features:

  * •

In general, cryptography and encryption are complex and vulnerable.

  * •

Cryptography and encryption (e.g., symmetric and asymmetric) require
complicated operations (resources and capabilities) at Tx and Rx. Security can
be compromised by inexhaustible computing power, network latency, and network
overhead.

  * •

Hierarchical decentralized architectures, delay-sensitive and power-limited
applications, and large-scale and highly mobile systems are unsuitable.

In contrast, PLS-based solutions are characterized by the following key
features:

  * •

PLS can complement or replace cryptography. Secure transmission can be
achieved through signal design and signal processing (no key management,
exchange, or maintenance required).

  * •

PLS solutions (e.g., beamforming, AN, and resource allocation) may not require
computational operations in the end users.

  * •

PLS is simple, flexible, scalable, and lightweight, reducing complexity and
overhead. It offers security solutions that are cost-effective while balancing
implementation complexity.

  * •

A suitable solution for applications with power- and delay-sensitive
requirements, as well as evolving physical layers and networking
infrastructures (where security is implemented at the edge).

As each type of secure solution has advantages and disadvantages, selecting a
specific approach will depend on the security requirements, type of security
attack, and type of communication system. A combined approach can often
provide an advanced security level [15]. From an information-theoretic
perspective, PLS has garnered considerable interest in industrial, academic,
and research communities over the last few decades, providing unbreakable,
proven, and quantifiable security [16].

####  II-B2 Leveraging Sources for Security

Random and time-varying wireless channels with limited coherence distance,
time, and bandwidth present challenging propagation environments.
Nevertheless, independent channels with distinct observable parameters are
crucial for data security. Additionally, a wireless link can be authenticated
or secured using the unique characteristics of the RF front end and the
physical properties of the radio environment. For example, hardware
fingerprints, such as phase noise, carrier frequency offset, in-
phase/quadrature imbalance, non-linearity of the power amplifier, and antenna
imperfections, can complement channel-based PLS methods [17]. Particularly,
channel-based PLS methods are more appropriate for relatively stationary and
indoor environments (e.g., IoT). In contrast, RF-based PLS methods provide
more stable and secure solutions in rapidly changing environments. Neither of
these types of PLS introduces unnecessary overhead, as channel estimation is
an integral part of wireless communications, as well as measuring the
imperfections in the RF transceivers to ensure reliable communications.
Moreover, physical measurements such as distance, speed, angle, size, and
constituent materials can be incorporated into environment-based PLS methods
[18]. In this article, we focus on channel-based PLS solutions to address the
security of 6G-IoT networks by leveraging the propagation characteristics of
wireless channels with RIS.

####  II-B3 Security Requirements and Malicious Attacks

A top-level taxonomy of confidentiality and availability can be used to
describe the requirements for secure communication systems. Confidentiality
refers to exposing information to Rx without being detected by an
eavesdropper. Availability refers to the ability of the Tx to access wireless
networks at any time and from anywhere. Owing to the broadcast and
superposition properties of wireless networks, wireless transmissions are
susceptible to various security breaches, including passive eavesdropping
attacks (intercepting data) and active jamming attacks (disrupting
transmissions) [19]. Fig. 3 illustrates certain examples of eavesdropping and
jamming attacks in which the eavesdropper overhears the information without
taking any proactive measures while the jammer injects an interfering signal
to block legitimate transmissions. The transmission of high-power noise or
false data by a jammer result in active malfunctions, in which the Tx cannot
access the channel, or the Rx receives distorted information.

Figure 3: Physical layer attacks: a) Eavesdropping attacks; b) Jamming
attacks.

####  II-B4 Security Methods

Anti-jamming solutions make it difficult for jammers to disrupt legitimate
transmissions. Frequency hopping involves switching between different
frequencies in a predetermined pattern (or pseudo-random code-based) to make
the communications orthogonal to the jamming signal; a jammer is required to
hop between frequencies to track the signal. Spread spectrum spreads the
signal over a wide range of frequencies. Antenna diversity requires multiple
antennas at the Rx to improve signal reception. Redundancy refers to
transmitting the same information multiple times over different channels or
utilizing error-correcting codes to recover the transmitted information.
Physical separation involves separating the Rx by a large distance. Moreover,
ML algorithms can be trained to detect and classify jamming signals and adapt
transmission parameters in real-time to minimize jamming impact under varying
conditions, thereby making anti-jamming communications more robust, flexible,
and efficient. Generally, frequency hopping and spread spectrum require
additional spectral resources and are ineffective owing to the diversity and
dynamic nature of intelligent jammers. Furthermore, redundancy, antenna
diversity, and physical separation may not be feasible. By contrast, anti-
eavesdropping communications intentionally impair the reception of information
at the eavesdropper by using techniques such as beamforming, interference
cancellation, AN, resource control, directional antennas, and cooperative
communications [20]. In general, multi-antenna beamforming involves high
hardware costs, computational complexity, and energy consumption due to the
massive RF chains and complex signal processing requirements. AN and
cooperative communications present significant challenges related to
deployment scalability, hardware costs, and power consumption. In PLS methods,
the inherent randomness of the noise and fading variations in a wireless
channel are exploited. Because of this, they are less effective in unfavorable
propagation environments, which can be avoided by using RIS [21].

##  III RIS-Aided Physical Layer Security

This section presents insights into RIS-aided PLS in the 6G-IoT wireless
paradigm and discusses the theoretical background, literature overview, design
solutions against jamming and eavesdropping attacks, and simulation results.

###  III-A Background and Research Review

The RIS has many unique characteristics, including high-precision beamforming,
rapid deployment, limited control messages, real-time configuration,
affordable complexity, full-band response, FD transmission, superior power
gain (without imposing thermal noise), cost-effectiveness, and a high level of
energy and spectral efficiency. The RIS offers considerable performance
advantages and compatibility with PLS methods, making it an ideal candidate
for implementing and enhancing PLS [22]. In the straightforward RIS-aided PLS
design, the Tx communicates with the Rx over a vulnerable communication link,
while the RIS is deployed at a strategic location to exploit the
characteristics of the wireless channel. The RIS can protect against
eavesdropping by limiting the information of the secret messages extracted by
the eavesdropper by improving the signal-to-interference-plus-noise ratio
(SINR) at the Rx (by mitigating the fading effect and maintaining a stable
channel) and/or degrading the eavesdropper (by providing multi-path fading
attenuation over the channel). The RIS can also protect against jamming by
enhancing the SINR at the Rx to make the interfering signal less effective at
disrupting communications. Therefore, the optimal adjustment of the deployment
location, number of elements, and reflection coefficients for the RIS can
protect the Rx from jamming and eavesdropping attacks.

In terms of secure transmissions, RIS setups offer numerous advantages and
attractive features. The RIS-aided PLS solutions can be applied to key 6G
technologies such as mmWave/sub-THz communications, visible light
communications, vehicular communications, unmanned aerial vehicle (UAV)
communications, integrated sensing and communication, industrial IoT,
simultaneous wireless information and power transfer (SWIPT), non-orthogonal
multiple access (NOMA), and device-to-device (D2D) communications [9, 10, 11,
22, 23, 24, 25, 26, 27]. As presented in Table II, the improved PLS
performance under controlled channel gain through the RIS has been
demonstrated under several scenarios, systems models, methodologies,
performance metrics, and optimization problems [23, 24, 25, 26, 27, 28, 29,
30, 31, 32]. Despite extensive research efforts, the study and evaluation of
RIS-aided PLS in 6G-IoT networks are still at an early stage. The design and
implementation of RIS-aided PLS under variable network topologies and novel
application scenarios require further investigation. In particular, research
is required to determine the capabilities and limitations of design solutions,
and their effective implementations, inherent complexity, and additional
control variables.

TABLE II: Recent efforts toward the realization of 6G RIS-aided PLS. Ref. |  System model |  ———— PLS metric |  Design and optimization ——————- Design variables |  ————– Optimization methodology |  Insight remarks |  Security attack |  RIS-aided PLS design |  ——- Channel model |  Modeling ————– Perfect CSI: Rx, malicious users |  ——— RIS impairments
---|---|---|---|---|---|---|---|---|---|---
[23] |  THz-MIMO |  Secrecy rate |  Tx hybrid beamforming and RISs phase shift designs |  Iterative algorithms (Classical) |  Improved security for the blockage-prone THz |  Eavesdropping |  Beamforming |  Rayleigh |  $\checkmark$ $\times$ |  Ideal
[24] |  IOS-SWIPT IoT (SISO) |  Secrecy rate |  UAV trajectory and transmit and jamming powers, and IOS phase shifts |  Iterative SCA (Classical) |  IOS provides secure communications in full space |  Eavesdropping |  Friendly jammer |  Rician |  $\checkmark$ $\checkmark$ |  Ideal
[25] |  Aerial-RIS (SISO) |  Transmission rate |  Deployment and beamforming design for A-RIS |  Alternating optimization (Classical) |  Optimal ARIS design confirms jamming mitigation |  Jamming |  Passive beamforming |  Rayleigh |  $\checkmark$ $\checkmark$ |  Ideal
[26] |  Uplink SIMO |  System rate |  Transmission energy of Tx and phase shifts of RIS |  Deep Q network (ML) |  DQN algorithm can tackle the dynamic variations |  Jamming |  Passive beamforming |  Rayleigh |  $\checkmark$ $\checkmark$ |  Discrete phase shifts
[27] |  Multi-layer ITAN |  EE maximization problem |  User’s received decoder, terrestrial and aerial precoder, and RIS reflection |  Block coordinate descent (Classical) |  RIS-Tx can reduce cost, bring new DoF, and facilitate a large-scale array |  jamming and eavesdropping |  Passive beamforming |  Rayleigh |  $\checkmark$ $\times$ |  Ideal
[28] |  RIS-based back-scatter |  Minimize SINR of eavesdropper |  Transmit power at Tx and phase shift design at RIS |  Convex semi-definite program (Classical) |  Novel approach to modulate the received signal into jamming via RIS |  Eavesdropping |  Passive beamforming |  Rayleigh |  $\checkmark$ ($\times$ $\checkmark$) |  Ideal
[29] |  Multi-antenna BS and NOMA users |  Sum rate under QoS, RIS, and SIC constraints |  Tx beamforming with jamming and RIS reflection designs |  Iterative alternating optimization (Classical) |  Secrecy transmission via artificial jamming |  Eavesdropping |  Beamforming and jamming |  Rician |  $\checkmark$ $\times$ |  Ideal
[30] |  RIS-SISO network |  Secrecy outage probability |  RIS reflection coefficients, path loss, eavesdropper location distributions |  Closed-form analysis |  Secure communications with uncertain eavesdropper distributions |  Eavesdropping |  RIS beamforming |  Rayleigh |  $\checkmark$ $\times$ |  Discrete phase shifts
[31] |  RIS-SWIPT system |  Secrecy rate under EH and transmit power constraints |  Tx transmit power, user power splitting factors, and RIS phase shift matrix |  AO-based, and ML-based |  DL methods provide AO algorithm-based performance with lesser computation time |  Eavesdropping |  RIS beamforming |  Rayleigh and Rician |  $\checkmark$ $\checkmark$ |  Ideal
[32] |  STAR-RIS-NOMA network |  Secrecy rate |  AN precoding, STAR-RIS design, and users decoding order |  Iterative alternating optimization (Classical) |  STAR-RIS-aided AN designs for the users in full space |  Eavesdropping |  Full space beamforming via STAR-RIS |  Rayleigh |  $\checkmark$ $\checkmark$ |  Ideal

###  III-B RIS-Aided PLS: Design Solutions

In challenging scenarios, RIS-aided PLS solutions have demonstrated their
effectiveness when compared with conventional MIMO methods [27, 28, 29, 30,
31, 32]. This may include situations where the Rx has a higher secrecy rate
requirement, the eavesdropper/jammer contains more antennas (or has a better
channel condition), or the eavesdropper (jammer) is located closer to the Tx.
In such cases, PLS provisioning using large-scale antenna arrays may be
ineffective and spatial beamforming may not provide sufficient secrecy gain.
Furthermore, securing transmissions via combined methods, such as transmit
beamforming with AN and/or cooperative methods, may also be challenging.
Controlling the strength and direction of the reflected signal by using RIS
and performing signal processing and optimization for the Tx and Rx presents
novel security benefits. A properly configured RIS can optimize the radio
environment under challenging propagation conditions, enhancing the
effectiveness of wireless channels for secure communications regardless of the
number, location, and channel conditions of the Tx, Rx, and malicious nodes.
For example, if the nodes are less than a half-wavelength apart or the
propagation environment has poor scattering, independent observations cannot
be assumed owing to correlated channels. In such cases, a secure technique
alone may not protect against all types of attacks. Therefore, a strategy
involving the combination of different RIS-aided PLS solutions can provide
high-level security enhancements [30, 31, 32].

Figure 4: Illustration of RIS-aided PLS design solutions: a) RIS-based secure
resource allocation, b) RIS-based secure beamforming, c) RIS-based secure AN
injection, d) RIS-based secure cooperative relaying, and e) RIS-based secure
cooperative jamming.

####  III-B1 RIS-Based Secure Resource Management

The effective utilization of network resources (bandwidth, time, and power)
can ensure secure transmissions. As an example, link adaptation based on sub-
carrier and power allocation, adaptive modulation, and coding can achieve the
desired security objectives. PLS provision can be enabled by utilizing
transmission parameters optimized specifically for the Rx link using
independent fading. Because the signal intended for the Rx is optimized, it
inherently provides security against jamming and eavesdropping attacks without
requiring any additional processing or computation at the Rx [33, 34].
Furthermore, a more secure communication link can be established by adapting
the link between the Tx and Rx via RIS (e.g., by adjusting the transmission
parameters and the reflection characteristics). As illustrated in Fig. 4(a),
the RIS can steer the transmitted signals towards the Rx while simultaneously
canceling out the interference from the jammer and/or degrading the SNR at the
eavesdropper. Thus, the RIS can effectively create a ”secure zone” around the
Tx and Rx, making it much more difficult for an eavesdropper (or a jammer) to
intercept (or disrupt) the information. Specifically, RIS-based link
adaptation and channel-dependent resource allocation can support flexible and
scenario-specific secure transmissions and satisfy information security
requirements in terms of confidentiality and availability. In this regard,
certain parameters can be adjusted based on channel characteristics, such as
transmit power, modulation order, number of RIS elements and their reflection
coefficients, error correction, sub-carriers, coding rate, and RF bandwidth.

####  III-B2 RIS-Based Secure Beamforming

The spatial dimension of multiple antennas in MIMO systems introduces
signaling degrees of freedom that can be exploited to ensure the reliable
transmission of information. Through beamforming and precoding techniques, one
or more spatially directed signals are transmitted using an antenna array to
achieve diversity and array gains. Beamforming involves directing EM energy
toward a particular location by feeding the same signal to each antenna in the
array and steering the signal using phase shifters to adjust the beamforming
weights in real time. Precoding involves simultaneously transmitting multiple
symbols (i.e., rank-k transmission) and shaping the radiation pattern of the
antenna array at the Tx towards the Rx. In general, beamforming refers to a
single-user transmission with one data stream, and precoding refers to the
superposition of different beams for spatial multiplexing of multiple data
streams. Examples of linear precoding for multi-user MIMO systems include
generalized singular value decomposition, zero-force precoding, and dirty-
paper precoding [35]. These signal processing techniques can also boost PLS
performance by controlling the directivity or shape of the transmitted
signals. For example, an eavesdropper must be equipped with the same or better
MIMO configurations to intercept information transmitted over multiple spatial
channels. The passive beamforming of the RIS (the phase-shift design of the
elements) can complement the active beamforming of the Tx in providing PLS
performance. As shown in Fig. 4(b), the optimal beamforming vectors can be
designed to degrade the eavesdropping channel (relative to the legitimate
channel) to achieve confidentiality and enhance the power of the decoding
signal at the Rx while negating the impact of the jamming signal to achieve
availability. This can increase the difficulty for the eavesdropper and/or
jammer to intercept and/or disrupt information, respectively. Secure
transmissions can be more efficient by increasing the number of elements of
the RIS, compared to increasing the size of the antenna array of the Tx. For
example, using fewer antennas at the Tx can provide a certain degree of
secrecy gain when the RIS is deployed [36]. The higher the number of elements
at the RIS, the lower the number of antennas required at the Tx. RIS-based
secure beamforming designs can improve PLS performance metrics in different
system configurations (e.g., multi-stream, multi-user, and wide-band).
However, the use of a particular technique depends on the rank and security
requirements of the transmissions.

####  III-B3 RIS-Based Secure AN Injection

To prevent eavesdropping attacks, high-powered AN can be transmitted by a Tx
or Rx. Specifically, the Tx can transmit AN in the same frequency band as the
original signal by utilizing the null space in the MIMO channel between the Tx
and Rx, while the Rx can generate AN by adopting in-band FD communications.
For example, [37] deliberately introduced noise via the Rx by assuming noise-
loop modulation. Furthermore, [38] considered AN precoding, i.e., a known
interfering signal is added to the original signal at a Tx. Isotropic AN can
be adopted in scenarios where the channel state information (CSI) of the
eavesdropper is unknown. Otherwise, spatially selective AN can be adopted.
Most studies on AN-based PLS assume knowledge of the location of the
eavesdropper, which is not practicable. In a more realistic approach, the AN
can be designed solely based on the information of the Rx. The signal can,
however, still be intercepted by an attacker with sufficient advanced
technology or channel knowledge. As shown in Fig. 4(c), the RIS can
potentially reflect the AN and provide enough interference for the
eavesdropper to make intercepting the signal more difficult. Additionally, in
[38], the authors demonstrated the improvement in secrecy provided by Tx-
injected AN when the RIS is placed near the eavesdropper. It has been observed
in [39] that the RIS with AN design required fewer elements (and less
computation complexity) to achieve a certain level of secrecy compared to the
design without AN. In addition, AN is typically a power-intensive approach
because more transmission and circuit power are consumed. A constrained
transmit power is required to transmit a confidential message to the Rx while
injecting AN simultaneously, which can disrupt the decoding ability of the Rx.
By utilizing the RIS, the Tx can potentially reduce the transmit power
constraint, while maintaining high communication performance. The RIS-based AN
injection optimization process generally involves selecting the optimal beams
at the Tx/Rx (for transmitting/receiving the original signal and injecting the
AN) using real-time CSI and designing phase shifts for the RIS.

####  III-B4 RIS-Based Secure Cooperative Communications

Cooperative relaying and jamming communications can utilize spatial diversity
and provide efficient security solutions [40]. In cooperative relaying,
multiple trusted relays ameliorate the PLS by providing the cooperative
diversity benefits of MIMO technology in a distributed manner. To enhance
reception performance at the Rx against eavesdropping and jamming attacks,
relays can be selected based on their geographical locations or by using
directional antennas to direct a narrow beam of radiation toward the Rx. In
relay-based networks, the communication process is generally divided into two
orthogonal phases, in which the first phase involves the transmission from the
Tx (to both the relay and Rx) and the second phase involves the relay re-
transmitting the signal to the Rx. The AF and DF protocols are typically used
for relaying. The AF protocol is simpler to implement but suffers from noise
amplification. The DF protocol performs better when the relay is positioned
near the Tx and/or has better channel conditions. In accordance with
transmission and reception capabilities, relays can be HD or FD. The
advantages of relays have been extensively analyzed in challenging cases, such
as unavailable or expensive backhaul methods, site acquisition problems for BS
deployment, or high throughput requirements for cell edge or moving users.
Cooperative relaying presents several open issues, including the selection of
relays (which implies a trade-off between diversity benefit and security
gain), reliability, power control, placement, hardware imperfections, and
computational complexity. In addition to cooperative diversity, jamming
signals can also be transmitted through relays in a coordinated manner to
disrupt the interception of the eavesdropper. The open issues for cooperative
jamming include incentive mechanisms, power allocation under imperfect CSI,
and the design of jamming signals against multiple eavesdroppers. Furthermore,
hybrid cooperative relaying and jamming solutions can be used for PLS
provision. By controlling radio propagation via RIS as shown in Figs. 4(d) and
4(e), cooperative diversity benefits can be further enhanced to improve PLS
performance [41].
$\underline{Remark}$: The secrecy performance improvement achieved by RIS-
aided PLS designs can be a challenging problem owing to the high
dimensionality of the channel space, high-precision configuration of several
elements, strategic placement of the RIS, the coupled correlation of different
parameters, and the stringent hardware constraints of the RIS. The other
critical issues include the design of robust algorithms to adapt to external
factors such as interference and noise, as well as incorporate the essential
factors such as selection and/or scheduling of the relays in cooperative
communications, power allocation in AN, antenna selection in beamforming, and
the details of the RIS (e.g., geometry, orientation, surface material
properties, cost, and implementation complexity), and the environment (e.g.,
channel conditions, signal frequency, available resources, and malicious nodes
capabilities). The appropriate RIS-aided PLS solution can be selected based on
design scenarios and communication objectives, facilitating the trade-off
between security performance, hardware complexity, latency, and overhead
requirements.

Figure 5: Simulation results: a) Network topology, b) Secrecy rate vs. RIS
location: with and without RIS links, c) Secrecy rate vs. Number of RIS
elements, d) Secrecy outage probability vs. Number of RIS elements, e) Secrecy
rate vs. RIS location, and f) Secrecy outage probability vs. RIS location.

###  III-C Simulation Results

In this subsection, simulation results are presented to demonstrate the
effectiveness of the RIS against eavesdropping attacks. The network topology
including a Tx with a single antenna, an RIS with $N$ number of reflecting
elements, a (legitimate) Rx with a single antenna, and an eavesdropper with a
single antenna is illustrated in Fig. 5(a). Here, $d_{tr}$ is the distance
between a Tx and an RIS, $d_{te}$ represents the distance between a Tx and an
eavesdropper, and $d_{tl}$ is the distance between a Tx and an Rx. To prevent
channel blocking, the RIS can be positioned at an angle from the horizontal
line connecting the Tx, the eavesdropper, and the legitimate Rx. In this case,
if we consider the location of the Tx as an origin, that is, $(0,0)$, the
coordinates of the RIS are given by $\left(d_{tr},d_{tr}\tan(\theta)\right)$,
and the coordinates of the eavesdropper and the Rx are given by $(d_{te},0)$
and $(d_{tl},0)$, respectively. Furthermore, path loss and Rayleigh
distributions characterize large-scale and small-scale fading characteristics,
respectively. Specifically, the path loss is determined by

$L(d)=C_{0}\left(\frac{d}{d_{0}}\right)^{-\alpha}$ (1)

where $C_{0}=30$dB, $d_{0}=1$m, and path loss exponent
$(\alpha)=\\{2.5,3.0\\}$. Unless otherwise stated, the parameters are set as
transmit power $=20$dBm, noise variance $=-100$dBm, number of reflecting
elements ($N$) $=50$, and blocking loss in the direct links $=50$dB. If the
channel coefficient between the Tx and the $i$th RIS element is $h_{i}$, and
the channel coefficient between the $i$th RIS element and the Rx is $g_{i}$,
then the optimal phase to maximize the signal strength at the Rx is given by
$-\arg(h_{i}g_{i})$. With this phase shift, all signal components from the RIS
reflecting elements will have the same phase, thereby maximizing legitimate
SNR. In terms of PLS, this phase design is the optimal approach when the
eavesdropper’s channel is unavailable. In practice, continuous phase shift
values cannot be delivered from the Tx to the RIS. It is therefore assumed
that the quantized phase information is delivered to the RIS via a control
channel with limited bandwidth. In the simulations, 1, 2, and 3-bit
quantization schemes are employed. For example, 3-bit quantization chooses the
nearest phase among
$\\{0,\pm\frac{\pi}{4},\pm\frac{\pi}{2},\pm\frac{3\pi}{4},\pi\\}$. Moreover,
we assume that $h_{i}$ and $g_{i}$ for all $i\in\\{1,\cdots,N\\}$ are
available at the Tx.

Fig. 5(b) shows the improvement of the secrecy rate enabled by an RIS. The
simulation parameters are set as $d_{tr}=20$m, $d_{te}=50$m, and $d_{tl}=40$m.
The results present the secrecy rate versus the RIS location angle, $\theta$,
and demonstrate the effectiveness of RIS through the comparison of two
different cases of links, i.e., i) direct link only (without RIS) and ii) RIS
+ direct link (with RIS). In the results, we consider the unquantized RIS
phases. The higher $\alpha$ shows a lower secrecy rate because it degrades the
quality of the legitimate channel more significantly than that of the
eavesdropping channel. Moreover, as $\theta$ increases, the lower secrecy rate
is achieved because the indirect path via RIS has a longer distance. In any
case, we can achieve a much higher secrecy rate with RIS.

Figs. 5(c) and 5(d) show the secrecy rate and secrecy outage probability
performance, respectively, depending on the number of RIS elements ($N$) with
different quantization policies. Parameters are set as $d_{tr}=20$m,
$d_{te}=30$m, $d_{tl}=40$m, $\theta=10^{\circ}$, and $\alpha=2.5$. As the
number of RIS elements increases, the secrecy rate increases and the secrecy
outage probability decreases. For secrecy outage probability, the threshold of
the secrecy rate is set to be $2.5$ bps/Hz. The results also suggest that
3-bit quantization is sufficient to obtain the optimal secrecy performance
provided by RIS in a given simulation scenario.

Finally, Figs. 5(e) and 5(f) illustrate the secrecy rate and secrecy outage
probability, respectively, as a function of RIS location angle, i.e.,
$\theta$. Simulation parameters are set as $d_{tr}=20$m,
$d_{te}=\\{30,35\\}$m, $d_{tl}=40$m, and quantization bits for the RIS
reflection angle $=3$. For secrecy outage probability, the threshold of the
secrecy rate is set to be 3.0 bps/Hz. The results demonstrate that secrecy
rates decrease and secrecy outage probabilities increase with $\theta$ as the
distance to the RIS increases. Under a particular setup, an optimal $\theta$
maximizes the secrecy rate and minimizes the secrecy outage probability. For
example, when $\alpha=2.5$ and $d_{te}=30$m, $\theta=25^{\circ}$ will achieve
the highest secrecy rate and $\theta=30^{\circ}$ will achieve the lowest
secrecy outage probability. Furthermore, secrecy performances are more
affected by the path loss exponent than by the location of an eavesdropper.
Results also illustrate how secrecy performance is negatively and positively
impacted by $\alpha$ and $d_{te}$, respectively.

##  IV Research Issues and Possible Solutions

This section discusses the research issues and possible solutions for RIS-
aided secure 6G-IoT systems to provide design guidelines and useful insights.

###  IV-A RIS Modeling

####  IV-A1 Research Issues

The development of unified, physics-compliant, and ubiquitous hardware models
for RIS-aided PLS designs is an active research area. It is an intrinsically
complex task to analyze the functions of the RIS in different hardware
implementations (e.g., PIN diodes, antennas, and NTT Docomo smart glasses) and
to examine how the arbitrary EM field interacts with the RIS [42]. There has
been limited research on RIS modeling that incorporates important parameters
such as the coupling of the elements (based on the geometry and periodicity of
the RIS), impedance matching, hardware impairments (e.g., RF losses, and
limited or low-resolution phase shifts), and scattering properties of the
elements.

####  IV-A2 Possible Solutions

The phase shift, load impedance, and generalized sheet transition conditions
models can characterize the reconfigurability of elements and analyze the
boundary conditions of the EM field at RIS. Through the development of novel
hardware designs and manufacturing solutions, RIS systems can be made more
scalable and affordable while maintaining tuning capabilities and continuous
control. A quasi-continuous quantization method can be applied to amplitude
and phase controls or to phase-only controls, with an inherent trade-off
between implementation complexity and performance improvement.

###  IV-B Channel Modeling

####  IV-B1 Research Issues

A channel model integrates hardware design with communication theory and
describes the complicated interaction between wireless signals and the radio
environment mathematically. Path loss and multi-path fading are typically used
to describe large-scale and small-scale channel characteristics, respectively.
More research is required to develop numerically reproducible channel models
for RIS-assisted PLS in 6G-IoT systems [43].

####  IV-B2 Possible Solutions

A general standard channel modeling framework is required to accommodate
complex propagation phenomena associated with different application scenarios.
It may include large path loss and sparse multi-path components in mmWave/sub-
THz, non-stationary and channel hardening in massive MIMO, large channel space
and cascaded fading in RIS, high transmission loss underwater, random
fluctuations and non-line-of-sight propagation in industrial IoT, large
elevation and high mobility in UAVs, and a large coverage distance and delay
spread in satellites [44].

  * •

For accurate and tractable modeling, path loss models must incorporate the
impact and scaling laws of design parameters (e.g., the size of the RIS, the
placement of the RIS plane, the number of elements, and transmission
distances). As 6G RISs have relatively large apertures and higher operating
frequencies, near-field propagation, and indoor wireless solutions are
essential.

  * •

There is a lack of physical implementation-compliant and mathematically
traceable channel models required to evaluate the physical-layer performance
under different deployment scenarios, channel conditions, antenna settings,
network architectures, and system parameters. The channel models can be
classified as deterministic (e.g., measurement-based, ray-tracing-based, map-
based, and iterative-based) or stochastic (e.g., geometry-based and
correlation-based). Owing to the complexity of exact fading distributions in
complex environments, mathematical approximations are also critical [45].

###  IV-C Channel Estimation

####  IV-C1 Research Issues

In RIS-aided PLS design, the performance gain depends on the reconfiguration
of the RIS elements, which requires accurate, timely, and low-complex channel
estimation. The general assumption of perfect CSI for both Rx and malicious
users (eavesdropper and/or jammer) provides an upper bound on theoretical
performance. It is challenging to acquire perfect CSI with negligible training
overhead, computational complexity, and onboard signal processing. It is due
to hardware constraints and nonlinear characteristics of RIS elements, a lack
of information from malicious users (e.g., passive eavesdroppers are generally
unknown and do not cooperate with the Tx), and errors in channel estimation
and quantization that impair CSI estimation. The estimation of high-
dimensional channels in RIS-aided secure 6G-IoT systems has been examined
under various channel conditions and system scenarios [46].

####  IV-C2 Possible Solutions

Passive elements lack RF chains to transmit or receive pilot signals to
estimate unknown channel coefficients. To estimate individual channels via
training signals, an RIS can be equipped with low-power RF chains. Otherwise,
the concatenated channel can be estimated for a known RIS configuration by
sharing a common channel coefficient for nearby elements with a trade-off in
computational complexity and performance degradation. Thus, passive
beamforming can be designed for secure transmissions based on feedback from
the BS and users without explicitly estimating the individual channels. Pilot
sequences and training patterns are necessary for channel estimation
algorithms, such as least squares, minimum mean squared errors, and compressed
sensing. The spatially sparse nature of mmWave and sub-THz channels can reduce
training burden and performance error [47]. Nevertheless, accurate channel
models and hardware designs are needed. Low-dimensional, quasi-static, and
low-rank channel features can also be exploited to reduce pilot overhead.
Channel estimation for secure 6G-IoT systems using RIS can aim at the
following:

  * •

Channel estimation methods with optimal performance and complexity trade-offs
under different signal models and system configurations are crucial.
Developing a training signal and estimation algorithm jointly is not trivial.
Thus, optimizing a training signal for a particular estimation algorithm can
be efficient.

  * •

The challenges associated with channel estimation include phase noise, spatial
correlation, absence of information from jammers and/or eavesdroppers,
configuration of reflecting elements in real time, deployment of large-scale
RIS at random locations, and propagation characteristics of mmWave and sub-THz
signals.

  * •

An imperfect CSI deteriorates security performance. Owing to this, it is
imperative to design robust PLS designs under imperfect or error-prone CSI.
However, uncertainty constraints associated with imperfect CSI models
complicate optimization problems.

###  IV-D Optimization Approaches

####  IV-D1 Research Issues

The optimization of RIS-aided PLS designs constitutes a challenging
mathematical problem owing to the multiple and coupled variables, which can be
related to resource allocation (e.g., sub-carrier and power allocation), RIS
(e.g., size, placement plane, and element number), beamforming (e.g., antenna
selection), AN (e.g., power allocation factors), and cooperative
communications (e.g., relay selection and/or placement). Non-convex objective
functions (e.g., performance metrics, such as secrecy capacity, secrecy outage
probability, and secure energy efficiency) and/or non-convex constraint
functions (such as source power constraint, phase shift modulus constraint,
and discrete constraint for RIS elements) make optimization problems non-
convex and non-trivial. Fading channels usually require complex functions,
making the analysis further complicated by the large channel space required
for many users and various RIS elements. In addition, the complex topologies
and nonlinear components in 6G increase the complexity of global optimization
[48].

####  IV-D2 Possible Solutions

In RIS-aided secure 6G-IoT systems, optimization problems are diverse and
complex, making it unacceptable to use exact and simple convex optimization
methods, such as linear programming, dynamic programming, exhaustive search,
and interior point analysis. Therefore, effective methods are needed to solve
high-dimensional/multi-objective optimization problems. An analysis can be
simplified by excluding hardware impairments and imperfect conditions, such as
the phase-dependent amplitude of the RIS, discrete amplitude and phase
adjustments of the RIS, transceiver hardware impairments, channel estimation
error, and correlation among channels. Intractable and non-convex optimization
problems can be solved analytically with approximation and relaxation
techniques. Using iterative search algorithms or heuristic algorithms, a non-
convex optimization problem can be transformed into approximate sub-problems
under convex relaxation (using approximate sub-problems). It is possible to
categorize optimization methods into integer programming (with integer
decision variables), quadratic programming (with quadratic objective
functions), mixed-integer programming (with discrete and continuous
variables), a difference of convex functions programming (with objective
functions as the subtraction of two convex functions), fractional programming
(with the ratio of two nonlinear functions), and semidefinite programming
(with the problems under linear equality and non-negative constraint
functions). Non-convex optimization problems can also be handled iteratively
using iterative algorithms and alternative optimization techniques, such as
the penalty function method, dual decomposition, semidefinite relaxation, and
successive convex approximation [49].

###  IV-E ML-Based approaches

####  IV-E1 Research Issues

The analytical solutions rely on complex mathematical models that approximate
the real environment and are only applicable under certain conditions owing to
limited working flexibility. In addition, conventional solutions may not work
for complex RIS-aided PLS designs because of RIS configurations, dynamic radio
channels, and malicious user behavior. In contrast, ML-based solutions are
ideal for designing and optimizing RIS-aided secure 6G-IoT systems, addressing
Rx-side optimization, RIS-side passive beamforming, channel modeling and
estimation, and combating jammers and eavesdroppers of unknown types [50].

####  IV-E2 Possible Solutions

Deep learning (DL) can improve channel estimation, tracking, and prediction
under imperfect environments. Model-free mapping is possible with DL
algorithms by using learnable parameters to alleviate uncertainties (e.g.,
rapidly changing scattering environments and hardware impairments) and yield
flexible results. The DL algorithms can learn RIS channels efficiently in any
system configuration and signal model and present better estimation
performance under complex channel conditions (e.g., non-stationary and
nonlinear temporal dynamics, and spatial correlations). Owing to its large
search space and learning and mapping capabilities, deep reinforcement
learning (DRL) can solve complex and high-dimensional optimization problems
efficiently. Furthermore, DRL algorithms can handle problems with multiple
(conflicting) objectives, such as joint optimization of coverage and secrecy
capacity, and can control both continuous and discrete actions. However, a
large channel space in RIS-aided secure 6G-IoT systems requires complicated
training processes. Moreover, offline-trained models are more complex, consume
more power, and do not match real-world conditions. Further research is
required to develop learning algorithms that are less complex (e.g., with a
lower training overhead) while maintaining a high level of accuracy and
stability.

Figure 6: An overview of STAR-RIS and malicious RIS for a secure 6G-IoT
network.

##  V Recent Advances

This section discusses the recent advances in terms of the STAR-RIS and
malicious (illegal) RIS from the perspectives of eavesdropping and jamming
attacks.

###  V-A STAR-RIS

Reflecting-only RIS requires the Tx and Rx to be deployed on the same side.
Therefore, service coverage cannot be provided for the Rx behind the RIS.
STAR-RIS is a new paradigm for wireless communication that presents more
degrees of freedom by manipulating the signals in a full space. STAR-RISs can
provide both transmission (T) and reflection (R) signals to serve the users
located on both sides [51]. From the hardware manufacturing perspective, STAR-
RIS can enable electric and magnetic currents to produce T and R signals
simultaneously or consecutively. Three operating protocols, energy splitting,
mode selection, and time splitting, can achieve different communication modes
through coupled or independent controls. Fig. 6 demonstrates the effectiveness
of the STAR-RIS against jamming and eavesdropping attacks. However, RIS-aided
PLS designs cannot be directly applied to the STAR-RIS-aided PLS because STAR-
RIS introduces adjustable parameters for both the T and R links. STAR-RIS-
aided PLS designs can be directed as follows:

  * •

The modeling and analysis of the STAR-RIS-aided PLS designs are still at an
early stage. For example, the manipulation of both the T and R signals
requires different hardware implementations (with different tuning mechanisms
for the elements), channel modeling (to compute secure communications for the
users in the T and R half-spaced regions), channel estimation (to design both
T and R patterns), and its performance evaluation (e.g., pilot overhead,
accuracy, and complexity depend on the number of users, elements, and
operating protocols).

  * •

The implementation of the STAR-RIS-aided PLS designs requires further
research. Specifically, the beamforming and deployment of the STAR-RIS for the
PLS in the presence of multiple users in a full space are far more complicated
research problems. The selection of the appropriate STAR-RIS operating
protocol based on the communication objective or a suitable balance between
the design complexity and system performance is crucial.

  * •

The optimization problems of the STAR-RIS-aided PLS are complicated owing to
the fundamental theoretical constraints of the STAR-RIS (e.g., coupled phase
constraint in the ES protocol, unit modulus constraint in the TS and MS
protocols), and constraints due to the location uncertainties of the
eavesdropper and jammer. Specifically, beamforming designs with coupled T and
R coefficients require novel convex and ML-based optimization algorithms owing
to the requirement for a hybrid (i.e., continuous and discrete) control
scheme.

###  V-B Malicious RIS

Rx and malicious nodes can exploit the RIS, such that both legitimate RIS and
malicious (illegal) RIS can exist in the 6G-IoT wireless paradigm [52]. Fig. 6
depicts the possible vulnerabilities of the configuration (access) of the RIS.
The reasons may include its low hardware cost, imperfect reconfigurability of
elements, and illegal deployment. Malicious RIS can facilitate jamming attacks
by substantially improving the interference powers at the Rx or by extending
the coverage of the interference signal. Effective reflection can cause
deterioration (reduce the SINR at the Rx) by constituting (altering) the phase
shift metric either by making it orthogonal (to the actual one) or by
injecting the complex Gaussian distributed phase shifts. To facilitate an
eavesdropping attack, malicious RIS can be deployed for more information
leakage. This can be achieved by using an information-gathering approach
(e.g., adopting CSI of the eavesdropper) to maximize the SNR of the
eavesdropper instead of the Rx.

The adverse applications of the malicious RIS can be applied in both data
transmission and pilot contamination. Malicious RIS can rapidly reshape
illegitimate communication links and significantly increase the complexity of
the PLS provision problem. For example, it is impossible to design the
legitimate RIS to cancel the signaling of the malicious RIS owing to the
covert deployment (and the absence of the CSI of the malicious RIS links).
Furthermore, malicious RIS can considerably hinder pilot-based CSI methods and
degrade the channel estimation accuracy of legitimate links. This disables the
RIS-based channel estimation and beamforming schemes, making the optimization
problems based on instantaneous CSI insufficient and outdated. Therefore, the
adverse impact of the malicious RIS requires further analysis, and effective
countermeasures are required to establish RIS-aided secure 6G-IoT networks.
For example, optimization problems against jamming and eavesdropping attacks
can be designed based on an imperfect CSI. In addition, case studies and
performance evaluations can be conducted to validate the effectiveness of the
RIS-aided PLS solutions in the presence of malicious RIS.

##  VI Conclusion and Future Work

6G-IoT networks pose severe security risks owing to the broadcast transmission
of confidential information and increased attack vectors. RIS and PLS can be
combined to deliver lightweight security for IoT devices. This article
analyzes RIS-aided PLS designs against eavesdropping and jamming attacks. The
fundamental principles and hardware architecture of RIS and security concepts
and methods of PLS are discussed. We present RIS-aided PLS design solutions
based on resource allocation, beamforming, artificial noise, and cooperative
communication. In addition, we discuss the research issues and potential
solutions related to RIS modeling, channel modeling and estimation,
optimization, and ML, as well as recent advancements, e.g., STAR-RIS and
malicious RIS. In the future, the design solutions will be validated using
hardware platforms and experiments and the research will be extended to
practical settings, such as mmWave/THz propagation, RIS hardware limitations,
eavesdroppers and jammers using ML and malicious RISs, and large-scale
networks.

## Acknowledgments

This research was supported by the Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education(MOE) (NRF-2022R1I1A1A01071807, 2021R1I1A3041887), by the Institute
of Information & communications Technology Planning & Evaluation (IITP) grant
funded by the Korea government (Ministry of Science & ICT (MSIT))
(2022-0-00704, Development of 3D-NET Core Technology for High-Mobility
Vehicular Service), and by Korea University Grant.

## References

  * [1] H. Yu and J. Joung, “Secure IoT communications using HARQ-based beamforming for MISOSE channels,” IEEE Internet Things J., vol. 8, no. 23, pp. 17211–17226, May 2021.
  * [2] H. Yu and J. Joung, “Design of the power and dimension of artificial noise for secure communication systems,” IEEE Trans. Commun., vol. 69, no. 6, pp. 4001–4010, Mar. 2021.
  * [3] W. Khalid, H. Yu, J. Cho, Z. Kaleem, and A. Shabbir, “Rate-energy tradeoff analysis in RIS-SWIPT systems with hardware impairments and phase-based amplitude response,” IEEE Access, vol. 10, pp. 31821–31835, Mar. 2022.
  * [4] L. Wang, S. -W. Qu, J. Li, Q. Chen, Q. Yuan, and K. Sawaya, “Experimental investigation of MIMO performance using passive repeater in multipath environment,” IEEE Antennas Wirel. Propag. Lett., vol. 10, pp. 752–755, Jul. 2011.
  * [5] W. Khawaja, O. Ozdemir, Y. Yapici, F. Erden, and I. Guvenc, “Coverage enhancement for NLOS mmWave links using passive reflectors,” IEEE Open J. Commun. Soc., vol. 1, pp. 263–281, Jan. 2020.
  * [6] E. Bjornson, O. Ozdogan, and E. G. Larsson, “Intelligent reflecting surface versus decode-and-forward: How large surfaces are needed to beat relaying?,” IEEE Wireless Commun. Lett., vol. 9, no. 2, pp. 244–248, Feb. 2020.
  * [7] L. Xu, X. Zhou, Y. Tao, X. Yu, M. Yu, and F. Khan, ‘AF relaying secrecy performance prediction for 6G mobile communication networks in industry 5.0,” IEEE Trans. Industr. Inform., vol. 18, no. 8, pp. 5485–5493, Aug. 2022.
  * [8] R. Zhong, Y. Liu, X. Mu, Y. Chen, X. Wang, and L. Hanzo, “Hybrid reinforcement learning for STAR-RISs: A coupled phase-shift model-based beamformer,” IEEE J. Sel. Areas Commun., vol. 40, no. 9, pp. 2556–2569, Sep. 2022.
  * [9] V. -L. Nguyen, P. -C. Lin, B. -C. Cheng, R. -H. Hwang and Y. -D. Lin, “Security and privacy for 6G: A survey on prospective technologies and challenges,” IEEE Commun. Surv. Tutor., vol. 23, no. 4, pp. 2384–2428, Aug. 2021.
  * [10] M. Mitev, A. Chorti, H. V. Poor and G. P. Fettweis, “What physical layer security can do for 6G security,” IEEE Open J. Veh. Technol., vol. 4, pp. 375–388, Feb. 2023.
  * [11] L. Mucchi et al., “Physical-layer security in 6G networks,” IEEE open j. Commun. Soc., vol. 2, pp. 1901–1914, Aug. 2021.
  * [12] Y. Cao, S. Xu, J. Liu and N. Kato, “Toward smart and secure V2X communication in 5G and beyond: A UAV-enabled aerial intelligent reflecting surface solution,” IEEE Veh. Technol. Mag., vol. 17, no. 1, pp. 66–73, Mar. 2022.
  * [13] J. Kim, H. Yu, X. Kang, and J. Joung, “Discrete phase shifts of intelligent reflecting surface systems considering network overhead,” Entropy, vol. 24, no. 12, Nov. 2022, Art no. 1753.
  * [14] W. Khalid, H. Yu, and S. Noh, “Residual energy analysis in cognitive radios with energy harvesting UAV under reliability and secrecy constraints,” Sensors, vol. 20, no. 10, May 2020, Art no. 2998.
  * [15] H. Yu, T. Kim, and H. Jafarkhani, “Wireless secure communication with beamforming and jamming in time-varying wiretap channels,” IEEE Trans. Inf. Forensics and Secur., vol. 13, no. 8, pp. 2087–2100, Feb. 2018.
  * [16] H. Yu and I.-G. Lee, “Physical layer security based on NOMA and AJ for MISOSE channels with an untrusted relay,” Future Gener. Comput. Syst., vol. 102, pp. 611–618, Jan. 2020.
  * [17] W. Hou, X. Wang, J.-Y. Chouinard, and A. Refaey, “Physical layer authentication for mobile systems with time-varying carrier frequency offsets,” IEEE Trans. Commun., vol. 62, no. 5, pp. 1658–1667, May 2014.
  * [18] M. S. J. Solaija, H. Salman, and H. Arslane, “Towards a unified framework for physical layer security in 5G and beyond networks,” IEEE Open J. Veh. Technol., vol. 3, pp. 321–343, Jun. 2022.
  * [19] Y. Sun et al., “RIS-assisted robust hybrid beamforming against simultaneous jamming and eavesdropping attacks,” IEEE Trans. Wirel. Commun., vol. 21, no. 11, pp. 9212–9231, May 2022.
  * [20] W. Khalid and H. Yu, “Security improvement with QoS provisioning using service priority and power allocation for NOMA-IoT networks,” IEEE Access, vol. 9, pp. 9937–9948, Jan. 2021.
  * [21] W. Khalid, H. Yu, D.-T. Do, Z. Kaleem, and S. Noh, “RIS-aided physical layer security with full-duplex jamming in underlay D2D networks,” IEEE Access, vol. 9, pp. 99667–99679, Jul. 2021.
  * [22] A. K. Yadav, S. Yadav, A. Pandey, and A. Silva, “On the secrecy performance of RIS-enabled wireless communications over Nakagami-m fading channels,” ICT Express, 2022.
  * [23] J. Qiao, C. Zhang, A. Dong, J. Bian, and M.-S. Alouini, “Securing intelligent reflecting surface assisted terahertz systems,” IEEE Trans. Veh. Technol., vol. 71, no. 8, pp. 8519–8533, Aug. 2022.
  * [24] A. M. Benaya, M. H. Ismail, A. S. Ibrahim, and A. A. Salem, “Physical layer security enhancement via intelligent omni- surfaces and UAV-friendly jamming,” IEEE Access, vol. 11, pp. 2531–2544, Jan. 2023.
  * [25] X. Tang, D. Wang, R. Zhang, Z. Chu, and Z. Han, “Jamming mitigation via aerial reconfigurable intelligent surface: Passive beamforming and deployment optimization,” IEEE Trans. Veh. Technol., vol. 70, no. 6, pp. 6232–6237, May 2021.
  * [26] P. D. Thanh, H. T. H. Giang, and I.-P. Hong, “Anti-jamming RIS communications using DQN-based algorithm,” IEEE Access, vol. 10, pp. 28422–28433, Mar. 2022.
  * [27] Y. Sun et al., “Energy-efficient hybrid beamforming for multilayer RIS-assisted secure integrated terrestrial-aerial networks,” IEEE Trans. Commun., vol. 70, no. 6, pp. 4189–4210, Apr. 2022.
  * [28] S. Xu, J. Liu, and Y. Cao, “Intelligent reflecting surface empowered physical-layer security: Signal cancellation or jamming?,” IEEE Internet Things J., vol. 9, pp. 1265–1275, Jan. 2022.
  * [29] W. Wang et al., “Beamforming and jamming optimization for IRS-aided secure NOMA networks,” IEEE Trans. Wirel. Commun., vol. 21, no. 3, pp. 1557–1569, Mar. 2022.
  * [30] X. Gu et al., “Physical layer security for RIS-aided wireless communications with uncertain eavesdropper distributions,” IEEE Syst. J., vol. 17, no. 1, pp. 848–859, Mar. 2023.
  * [31] H. T. Thien, P. V. Tuan, and I. Koo, “A secure-transmission maximization scheme for SWIPT systems assisted by an intelligent reflecting surface and deep learning,” IEEE Access, vol. 10, pp. 31851–31867, Mar. 2022.
  * [32] Y. Han, N. Li, Y. Liu, T. Zhang, and X. Tao, “Artificial noise aided secure NOMA communications in STAR-RIS networks,” IEEE Wireless Commun. Lett., vol. 11, no. 6, pp. 1191–1195, Mar. 2022.
  * [33] Z. Kaleem, W. Khalid, A. Muqaibel, A. A. Nasir, C. Yuen, and G. K. Karagiannidis, “Learning-aided UAV 3D placement and power allocation for sum-capacity enhancement under varying altitudes,” IEEE Commun. Lett., vol. 26, no. 7, pp. 1633–1637, May 2022.
  * [34] C. B. Le et al., “Joint design of improved spectrum and energy efficiency with backscatter NOMA for IoT,” IEEE Access, vol. 10, pp. 7504–7519, Dec. 2021.
  * [35] Z. Kaleem, M. Ali, I. Ahmad, W. Khalid, A. Alkhayy, and A. Jamalipour, “Artificial intelligence-driven real-time automatic modulation classification scheme for next-generation cellular networks,” IEEE Access, vol. 9, pp. 155584–155597, Nov. 2021.
  * [36] M. Dajer et al., “Reconfigurable intelligent surface: design the channel – a new opportunity for future wireless networks,” Digit. Commun. Netw., vol. 8, no. 2, pp. 87–104, Apr. 2022.
  * [37] L. Mucchi, S. Caputo, P. Marcocci, G. Chisci, L. Ronga, and E. Panayirci, “Security and reliability performance of noise-loop modulation: Theoretical analysis and experimentation,” IEEE Trans. Veh. Technol., vol. 71, no. 6, pp. 6335–6350, Mar. 2022.
  * [38] E. Choi, M. Oh, J. Choi, J. Park, N. Lee, and N. Al-Dhahir, “Joint precoding and artificial noise design for MU-MIMO wiretap channels,” IEEE Trans. Commun., vol. 71, no. 3, pp. 1564–1578, Mar. 2023.
  * [39] X. Guan, Q. Wu, and R. Zhang, “Intelligent reflecting surface assisted secrecy communication: Is artificial noise helpful or not?,” IEEE Wireless Commun. Lett., vol. 9, no. 6, pp. 778–782, jan. 2020.
  * [40] F. Jameel, S. Wyne, G. Kaddoum, and T. Q. Duong, “Comprehensive survey on cooperative relaying and jamming strategies for physical layer security,” IEEE Commun. Surv. Tutor., vol. 21, no. 3, pp. 2734–2771, Aug. 2019.
  * [41] J. Guo et al., “RIS-assisted secure UAV communications with resource allocation and cooperative jamming,” IET Commun., vol. 16, no. 13, pp. 1582–1592, May 2022.
  * [42] Y. Liu et al., “Reconfigurable intelligent surfaces: Principles and opportunities,” IEEE Commun. Surv. Tutor., vol. 23, no. 3, pp. 1564–1577, May 2021.
  * [43] M. Shahjalal et al., “Enabling technologies for AI-empowered 6G massive radio access networks,” ICT Express, 2022.
  * [44] W. Khalid and H. Yu, “Spatial–temporal sensing and utilization in full duplex spectrum-heterogeneous cognitive radio networks for the internet of things,” Sensors, vol. 19, no. 6, Mar. 2019, Art no. 1441.
  * [45] W. Khalid and H. Yu, “Sum utilization of spectrum with spectrum handoff and imperfect sensing in interweave multi-channel cognitive radio networks,” Sustainability, vol. 10, no. 6, May 2018, Art no. 1764.
  * [46] S. Noh, J. Lee, G. Lee, K. Seo, Y. Sung, and H. Yu, “Channel estimation techniques for RIS-assisted communication: Millimeter-wave and sub-THz systems,” IEEE Veh. Technol. Mag., vol. 17, no. 2, pp. 64–73, Apr. 2022.
  * [47] S. Noh, J. Lee, H. Yu, and J. Song, “Design of channel estimation for hybrid beamforming millimeter wave systems in the presence of beam squint,” IEEE Syst. J., vol. 16, no. 2, pp. 2834–2843, Jun. 2022.
  * [48] R. Alghamdi et al., “Intelligent surfaces for 6G wireless networks: A survey of optimization and performance analysis techniques,” IEEE Access, vol. 8, pp. 202795–202818, Oct. 2020.
  * [49] W. Khalid and H. Yu, “Optimal sensing performance for cooperative and non-cooperative cognitive radio networks,” Int. J. Distrib. Sens. Netw., vol. 13, no. 11, Nov. 2017, Art no. 1550147717744992.
  * [50] H. Yang, Z. Xiong, J. Zhao, D. Niyato, L. Xiao, and Q. Wu, “Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 375–388, Jan. 2021.
  * [51] W. Khalid, Z. Kaleem, R. Ullah, T. V. Chien, S. Noh, and H. Yu, “Simultaneous transmitting and reflecting- reconfigurable intelligent surface in 6G: Design guidelines and future perspectives,” IEEE Netw., 2022.
  * [52] Y. Wang, H. Lu, D. Zhao, Y. Deng, and A. Nallanathan, “Wireless communication in the presence of illegal reconfigurable intelligent surface: Signal leakage and interference attack,” IEEE Wireless Commun., vol. 29, no. 3, pp. 131–138, Jun. 2022.