XX XX 8 May/June 00 xxxx 0000 00 xxxx 0000 MCE.2022.Doi Number

Special Section: Security and Privacy-Aware Emerging Computing Editor: Name,
xxxx@email

# AI-Oriented Two-Phase Multi-Factor Authentication in SAGINs: Prospects and
Challenges

Bin Yang  Zhejiang Lab, Hangzhou, China  Shanyun Liu  Zhejiang Lab, Hangzhou,
China  Tao Xu  Zhejiang Lab, Hangzhou, China  Chuyu Li  Zhejiang Lab,
Hangzhou, China  Yongdong Zhu  Zhejiang Lab, Hangzhou, China  Zipeng Li
Nanchang University, Nanchang, China  Zhifeng Zhao  Zhejiang Lab, Hangzhou,
China

(2022)

###### Abstract

Space-air-ground integrated networks (SAGINs), which have emerged as an
expansion of terrestrial networks, provide flexible access, ubiquitous
coverage, high-capacity backhaul, and emergency/disaster recovery for mobile
users (MUs). While the massive benefits brought by SAGIN may improve the
quality of service, unauthorized access to SAGIN entities is potentially
dangerous. At present, conventional crypto-based authentication is facing
challenges, such as the inability to provide continuous and transparent
protection for MUs. In this article, we propose an AI-oriented two-phase
multi-factor authentication scheme (ATMAS) by introducing intelligence to
authentication. The satellite and network control center collaborate on
continuous authentication, while unique spatial-temporal features, including
service features and geographic features, are utilized to enhance the system
security. Our further security analysis and performance evaluations show that
ATMAS has proper security characteristics which can meet various security
requirements. Moreover, we shed light on lightweight and efficient
authentication mechanism design through a proper combination of spatial-
temporal factors.

††journal: IEEE Consumer Electronics Magazine

With the prosperity of the fifth-generation (5G) mobile communication
networks, the number of 5G subscribers has already reached an enormous scale,
leading to a significant shift in people’s daily lives. Nowadays, academia and
industry are turning their attention to emerging next-generation systems. It
is envisioned that the sixth-generation (6G) communications will support five
application scenarios: Enhanced Mobile Broadband Plus, Big Communications,
Secure Ultra-Reliable Low-Latency Communications, Three-Dimensional Integrated
Communications, and Unconventional Data Communications [1]. Space-air-ground
integrated networks (SAGINs) leverage the advantages of satellite networks
(including geosynchronous Earth orbit (GEO) satellites, medium Earth orbit
(MEO) satellites, low Earth orbit (LEO) satellites, and their mutual links),
high altitude platforms, and terrestrial communication systems. SAGINs are
therefore considered as a promising architecture to support ubiquitous,
seamless, reliable and high-data-rate services anytime and anywhere in 6G
communication networks. Remarkably, practical experiments and ambitious
projects have been initiated to offer ubiquitous Internet services through
SAGINs, such as Google Loon, Thales Stratobus, OneWeb, and SpaceX.

SAGIN is a highly heterogeneous and multidimensional network compared to
conventional terrestrial or satellite networks. It has wide coverage,
broadcast channels, and various network entities, including networking
infrastructure and connected Internet of Thing (IoT) devices. However, its
wide coverage feature brings trust and safety issues. Specifically, the
wireless signal in SAGIN propagates mainly in free space. In other words, not
only authorized users could receive the information but also malicious users
could obtain wireless power and retrieve secure information from power leakage
in wireless signals. Besides, intrinsic trust and data reliability issues may
arise in SAGIN during multi-hop transmissions among distrustful entities
within each segment or across segments. Due to the lack of unified security
precautions, interconnected intelligent devices can be vulnerable to various
cyber attacks, and traceable data provenance is difficult [2]. Therefore,
merits including security, privacy, and intelligence have become the crux of
determining whether SAGIN can continue to evolve healthily.

As the first security perimeter, authentication is a pivotal method to
identify the legitimacy of IoT devices that access the network, thereby
enabling real-time monitoring and promoting collaborative sharing [3, 4].
Authentication can be classified into one-shot authentication and continuous
authentication based on the duration of the authentication process. One-shot
authentication is an elementary authentication mechanism that identifies IoT
devices using crypto-based techniques such as passcodes, PINs, and
fingerprints. However, it only protects the security of SAGIN during the
initial access process and cannot guarantee security during the operational
phase. With the proliferation of intelligent IoT devices, especially wearable
devices like fitness bands and augmented reality (AR) glasses, personal
behavioral and physiological biometrics can be easily collected by built-in
sensors, which promotes continuous authentication from concept to
implementation. As an enhancement and supplement to one-shot authentication,
continuous authentication defenses against attacks in the background without
user intervention by constantly validating access devices according to the
historical features of users. To cope with the new features of SAGINs, it
becomes increasingly essential to combine one-shot authentication for quick
access and continuous authentication for sustainable security together,
supporting convenient access and security robustness of various kinds of
communication entities [5].

To adequately utilize the collected data from IoT devices, adopting multi-
factor authentication (MFA) is promising. This means that multiple
heterogeneous validation methods can be combined intelligently to grant or
deny access reliably [6]. In MFA, three types of factor groups, i.e.,
knowledge factor, ownership factor and biometric factor, are available to
connect an individual with the established credentials. Knowledge factor,
ownership factor and biometric factor refer to factors like passwords, tokens
and behavioral patterns, respectively. Specifically, spatial-temporal
features, such as geographical position, Doppler shift and traffic volume, are
likely to be utilized in the continuous authentication phase due to the
critical roles of SAGINs in providing varieties of vertical applications by
connecting enormous heterogeneous devices, machines, and industrial processes.
Through MFA by artificial intelligence (AI) techniques, including supervised
learning, unsupervised learning, and reinforcement learning, trusted
communications and services can be accomplished to adapt to dynamic
environments.

Driven by the limitations of conventional authentication mechanisms and the
induced demands in an integrated network, this article presents a novel
authentication framework for SAGINs, where an AI-oriented two-phase multi-
factor authentication scheme (ATMAS) is proposed. In ATMAS, a machine-
learning-based continuous authentication which refers to ’Phase II’ is
performed to intelligently grant or deny access reliably by capturing user-
profiles and traits, following a conventional cryptographic authentication
named ’Phase I’. The contributions of this article are summarized as follows:

  * •

We propose the ATMAS based on the characteristics of SAGINs. For the ATMAS,
four communication entities, i.e., mobile user (MU), base station (BS),
satellite, and network control center (NCC) are considered, in which satellite
and NCC cooperate with each other to implement authentication.

  * •

Through the design of ATMAS, we leverage the unique spatial-temporal features
of SAGIN in Phase II, including service and geographic features. This allows
us to ensure the security of the SAGIN from login to logout.

  * •

To evaluate the robustness and validate the security of the proposed scheme,
we conduct security analysis by analyzing the security characteristics.
Moreover, from the perspective of performance evaluation, we shed light on
lightweight and efficient authentication mechanism design by a proper
combination of spatial-temporal factors.

The remainder of this article is organized as follows. Challenges of
authentication for the SAGINs are first introduced. Then we present the
advantages of AI-enhanced multi-factor continuous authentication. The design
of the proposed ATMAS and its security analysis are described in the following
Sections. In addition, simulation results are presented to evaluate the
performance of authentication accuracy. Finally, concluding remarks and future
research directions are provided.

Figure 1: Network Architecture of the SAGIN. The user/device segment shows the
trajectory of a MU and the changes of its location, traffic and biometric. The
ground/air/space access segment illustrates all kinds of access entities,
including BSs, high-altitude platforms and satellites.

## Challenges of Authentication for the SAGIN

Comprised of inter-satellite and inter-satellite links, the SAGIN is a highly
open wireless system. As satellites are exposed to the open environment, it is
easy to cause attacks such as link hijacking and entity counterfeiting, which
seriously affects the secure communication of the SAGIN. Reliable access
control plays a vital role in distinguishing the source nodes and addressing
identity-based attacks such as spoofing and Sybil attacks. In general, access
control can be decomposed into user authentication and authorization.
Authentication is a process that verifies that someone or something is who
they say they are and authorization is the security process that determines a
user or service’s level of access. The latter can be accomplished by methods
like enforcing allow or deny rules based on the user’s authorization level,
e.g., general user, super user and administrator, which is relatively less
complicated in the implementation stage. However, authentication brings about
a number of SAGIN-specific research questions that should be carefully
considered in this article.

Long Propagation and Processing Latencies. In SAGIN, the introduction of high-
altitude satellites leads to unidirectional propagation delays of around 15
ms, even for the links between LEO satellites and IoT devices. This results in
round-trip delays of approximately 50 ms for IoT devices, which significantly
reduces quality of service (QoS) in latency-sensitive scenarios. Additionally,
due to high mobility, the propagation delay is not permanent and varies with
the satellite’s position. Apart from propagation delay, conventional
cryptographic authentication requires increased communication and computation
overhead to cope with the increasing requirements of security, leading to long
processing latencies [7]. Such prolonged latencies are intolerable for
scenarios like disaster rescue and military missions. Therefore, lightweight
and selectable authentication mechanisms are urgently needed for these
latency-sensitive applications.

Insufficiency of Authentication Uniformity. As mentioned before, the SAGIN is
a highly heterogeneous network supported by different communication protocols.
Each network entity in the SAGIN encompasses a tremendous number of IoT
devices with various interfaces for control and management, which requires a
unified and pragmatic authentication mechanism when IoT devices access the
SAGIN from different locations. While a unified authentication framework has
been proposed for both the Third Generation Partnership Project (3GPP) and
non-3GPP access in [8, 9], sensitive data-related authentication must be
enforced to be implemented in the core network, which mitigates efficiency. As
a result, a new unified authentication scheme is necessary to adapt to the
heterogeneity of the SAGIN.

Inability to Achieve Real-time and Transparent Authentication. The traditional
crypto-based authentication verifies the legitimacy of IoT devices only at the
beginning of login based on a password, a personal identification number
(PIN), or a secret pattern. However, these methods are susceptible to
guessing, video capture, and spoofing as users often choose a simple password
for convenience. Moreover, traditional one-shot authentication mechanisms
cannot verify whether the logged-on user is the initially authenticated one
due to a lack of physiological and behavioral biometrics. To overcome these
shortcomings, users have to identify themselves by re-entering the password
periodically, which declines the experience and satisfaction of services. In
short, traditional authentication mechanisms lead to either security
vulnerabilities or inconvenience for the SAGIN. To achieve privacy protection
in the background, new mechanisms should be designed for real-time and
transparent security provisioning.

To put it succinctly, it is imperative to enhance the current authentication
mechanism for the SAGIN-specific system.

Figure 2: Authentication Phase of the ATMAS. In Phase I, a crypto-based
authentication is performed. In Phase II, an AI-based continuous
authentication including enrollment phase and authentication phase is
proposed.

## AI-Enhanced Multi-Factor Continuous Authentication

In the era of IoT, the SAGIN system has become more complex than traditional
information and communication technology platforms. To ensure the SAGIN
security from login to logout during IoT device activity, a continuous
authentication mechanism is indispensable to fulfill security requirements
without user intervention. Typically, continuous authentication validates
legitimate users based on behavioral and physiological biometrics using built-
in sensors on IoT devices. In recent years, due to advancements in storage and
computational resources, and the increasing diversity of sensors, continuous
authentication has become more effective and accurate by collecting, storing,
and analyzing massive amounts of data [10].

The current authentication mechanisms can be divided into two categories:
single-factor authentication and MFA, based on the features or authentication
factors applied. Single-factor authentication is a standard, low-security
method of authentication that requires matching only one factor, such as
password, to a username to get access to a system. However, single-factor
authentication provides a limited level of security because it is susceptible
to guessing, video capture, and spoofing. MFA is an enhanced authentication
mechanism that jointly conducts authentication utilizing multiple factors.
Factors in authentication can be classified into five categories: i. something
a user knows, ii. something a user possesses, iii. something a user is, iv.
something a user does and v. somewhere a user is [11]. With proper factor
selection and combination strategies, an efficient and high-security
authentication mechanism can be achieved.

By extracting the unique features of users from the abundant collected data,
AI algorithms including machine learning and deep learning are leveraged to
enhance the security of the SAGIN system. As illustrated in Fig. 2, the
pipeline of AI-based authentication mechanisms mainly consists of data
acquisition, data preprocessing, feature extraction, classification and
decision. Data acquisition involves sensors that sample real-world parameters
and convert them to electrical signals, which are then converted to digital
values. Data preprocessing is a critical procedure that distill high-quality
data from raw data, which is generally incomplete, noisy, inconsistent, and
redundant. To reduce noise and align output data, methods like clipping,
Z-score, and scaling are necessary [12]. Feature extraction means extracting
user features that present one’s identity or behavior from the collected
dataset. For classification, the goal is to learn a mapping function that
predicts label information for a given behavior sequence with minimal biases.
Finally, through evaluations of a verification system, the ultimate decision
can be made to grant access to legitimate users and deny access to impostors.
In this article, we envision ATMAS to address challenges for the SAGIN. The
advantages of ATMAS are summarized as follows.

Continuous Security. The proposed ATMAS not only authenticates at the initial
stage of login but also provides seamless protection to legitimate devices in
the background by adopting AI-enhanced algorithms.

High Flexibility. We utilize a conventional cryptographic authentication named
‘Phase I’ to quickly grant or deny service requester access, and an AI-aided
continuous authentication named ‘Phase II’ to intelligently verify legitimacy
during service operation. The SAGIN system operator is flexible in choosing
whether to use only ‘Phase I’ or to select a suitable number of authentication
factors based on security requirements.

High Robustness. By utilizing multiple factors in the procedure of continuous
authentication, it becomes difficult for adversaries to imitate or crack all
selected features in a single round of communication based on received signals
and observations.

## Design of the Proposed ATMAS in SAGINs

Before introducing the detailed design of the proposed ATMAS, we would like to
provide an overview of the architecture of the SAGIN, as illustrated in Fig. 1
and Fig. 2. The SAGIN system consists of three segments: the user/device
segment, the ground/air/space access segment and the authentication segment.
The user/device segment is an assemblage of various end mobile users and IoT
devices, and it is assumed that users and devices have the ability to sense
their locations, traffic and biometrics. The ground/air/space access segment
consists of all kinds of access entities, such as terrestrial BSs, high-
altitude platforms and satellites. The authentication segment is responsible
for one-shot authentication and continuous authentication. In this article,
the focus is on MEO/LEO satellites, which have relatively shorter transmission
delays compared to the GEO satellites. This allows satellites to provide
large-scale coverage, and beams of BSs or other relays ensure high-precision
user targeting [13]. The authentication procedure of the proposed ATMAS is
shown in Fig. 3, in which four network entities are involved: MU, BS,
satellite, and NCC. In the following subsections, the authentication procedure
will be discussed in detail.

Figure 3: Authentication Procedure of the ATMAS. The ATMAS consists of four
procedures, i.e., initialization and enrollment, registration, one-shot
authentication, and continuous authentication.

### Phase I: One-shot Authentication

We divide authentication of Phase I into three procedures: initialization and
enrollment, registration and one-shot authentication.

Initialization and Enrollment: The authentication in Phase I is cryptography-
based. NCC uses Elgamal encryption technology [14] to generate a private key
and a corresponding public key. The private key is stored by the NCC locally,
and the public key and its related key parameters are published to other
entities in SAGINs. Based on the key parameters, other entities can generate
their own private keys and public keys, respectively.

Registration: Before participating in the authentication procedure, entities
including MUs, BSs and satellites must register with the NCC. The BS and the
satellite have a similar registration procedure and we take satellite
registration as an example. After receiving a registration request from a
satellite, the NCC generates both a unique identity and a temporary identity
for the satellite. Authentication parameters, such as satellite identities and
shared public keys, are stored locally on the satellite. Next, the satellite
generates a timestamp and sends it, along with authentication parameters, to
the NCC. Upon receiving this information, the NCC first verifies whether the
transmission delay (i.e., the difference between the current time and the sent
timestamp) is less than the predefined delay threshold. If the transmission
delay does not meet the requirement, the NCC rejects the registration request.
Otherwise, the NCC checks where the authentication parameters sent by the
satellite coincide with parameters stored in the NCC’s local database. If the
parameters do not match, the satellite registration request is rejected.
Otherwise, another timestamp is generated and sent together with the
authentication parameters back to the satellite. When the satellite receives
the message from the NCC, it first verifies whether the transmission delay
from the NCC to the satellite meets the requirements. If so, the satellite
also verifies the authentication parameters received from the NCC. The
satellite registration procedure is considered validated if all checks pass.

For MU registration, we assume that MU and IoT devices have the ability to
collect raw biometric data. Firstly, the user selects an identity and a high-
entropy password, and utilizes a fuzzy extractor probabilistic key generation
algorithm to extract the key of biometric information. The MU uses a hash
function to generate authentication parameters of the combination of identity,
password and biometric features. The authentication parameters are transmitted
to the NCC. After receiving the information, the NCC verifies whether the
authentication parameters are the same as those stored locally. If so, the
authentication parameters will be calculated by a hash function and sent to
the MU. The MU authentication procedure is accomplished if the authentication
parameters are validated.

One-Shot Authentication: In this phase, when the MU requests to access the
SAGIN, the MU must provide the identity information, a password, and biometric
information for a handheld device. Then, the device generates a timestamp and
obtains the MU’s current location. A hash function is utilized to calculate
the key based on the combination of the above information. The key is then
sent to the satellite through the BS. After receiving the key, the satellite
first verifies whether the transmission delay is within the threshold. If not,
the MU access request is denied. Otherwise, the satellite sequentially
verifies the identity, location, password, and biometric information. If the
validation succeeds, authentication parameters and a timestamp are generated
and transmitted to the MU through the BS. Similarly, the MU checks the
timestamp and authentication parameters. If matched, the MU is marked as a
legal user and allowed to access the SAGIN.

### Phase II: Continuous Authentication

After the user gets access to the SAGIN, the NCC initiates a continuous
authentication request and a biometric/behavioral data acquisition request to
the MU. The collected data is then sent to the NCC through the BS. In the MU
registration phase, the NCC trains an AI-based model and extracts features of
the MU. In the continuous authentication phase, the NCC compares the real-time
extracted features with the stored features from the registration phase. Based
on the security level of the MU, the NCC determines the legality of the user,
and the transmission terminates if it is an illegal user.

Moreover, due to the latency requirement of the authentication, it is arduous
to use high-complexity algorithms. In phase II, a binary classification is
introduced where both spoofing actions and spoofers are seen as spoofing
attacks. For the sake of simplicity, we focus on this scenario as a
quintessential example to validate the proposed authentication scheme.

## Security Analysis

This section evaluates the security of the proposed authentication scheme and
demonstrates its ability to prevent security threats. In our proposed ATMAS,
we have implemented five security characteristics: mutual authentication,
forward security, resistance to replay attack, resistance to man-in-the-middle
attack, and data confidentiality and integrity. In addition, to demonstrate
the effectiveness of our proposed authentication scheme, we compare it with
existing counterparts in terms of security requirements and authentication
features. By doing so, we can show how our scheme offers improved security and
more robust authentication capabilities in comparison to existing solutions.

Table 1: Comparisons of Security Requirements and Authentication Features. | Li et al. [15] | Deng et al. [16] | Badhib et al. [17] | Our Scheme
---|---|---|---|---
Authentication Phase | One | One | Two | Two
Method | Crypto | Crypto | Crypto | Crypto, AI
Dynamic | $\times$ | $\times$ | $\surd$ | $\surd$
Mutual Authentication | $\times$ | $\times$ | $\surd$ | $\surd$
Forward Security | $\surd$ | $\surd$ | $\surd$ | $\surd$
Resistance to Replay Attack | $\surd$ | $\surd$ | $\surd$ | $\surd$
Resistance to Man-in-the-Middle Attack | $\surd$ | $\surd$ | $\surd$ | $\surd$
Data Confidentiality and Integrity | $\surd$ | $\surd$ | $\surd$ | $\surd$

  * •

Mutual Authentication: Mutual authentication implies that two participants can
authenticate each other. In the key agreement phase described in one-shot
authentication, the satellite can verify the legitimacy of the BS by checking
whether the hash result is consistent with the credential information saved
locally. Adversaries cannot get knowledge of the private key from public
values due to the properties of hash function. Thus, only the legitimate BS
who owns the private key can be authenticated by the satellite. Similarly, the
BS authenticates the satellite based on the authentication messages from the
satellite, which are encrypted by the satellite’s private key. The secure
mutual authentication between a MU and a BS can be guaranteed using a similar
analysis. Therefore, our scheme could provide secure mutual authentication.

  * •

Forward Security: Forward secrecy can ensure that session keys will not be
compromised even if the long-term secrets used in the session key exchange are
compromised. In our scheme, if an adversary learns the initial token and wants
to derive the initial token used in the previous session, they would need to
know the previously generated random numbers. However, the previously
generated random numbers cannot be obtained from the previous eavesdropped
messages as the hash is a one-way function. Therefore, our proposed scheme has
the property of forward secrecy.

  * •

Resistance to Replay Attack: Replay attack mainly refers to when a malicious
adversary uses its message regeneration ability to generate and replay a
message, thereby compromising protocol security. However, this attack can be
prevented by successfully authenticating the message through checking the
validity of its timestamp value. Additionally, because the hash function used
in initial authentication is unidirectional, an adversary is unable to fake
the message by modifying the timestamp value. Therefore, our proposed scheme
is capable of resisting replay attack.

  * •

Resistance to Man-in-the-Middle Attack: A man-in-the-middle attack means that
an adversary intercepts and selectively modifies communicated data to
masquerade as one entity involved in a communication session. In our scheme,
it is impossible for the adversary in the middle to register with the role
that already exists in the SAGIN. Besides, the secret keys of any entities in
the system cannot be obtained. As a result, the adversary cannot modify or
manipulate transmitting messages to invade the existing connection. Therefore,
the scheme would not be exposed to man-in-the-middle attacks.

  * •

Data Confidentiality and Integrity: Data confidentiality and integrity imply
that a message receiver can ensure the message has not been tampered with
during transmission. In our proposed scheme, the private information is
encrypted with the public key of the target entity. An adversary cannot
decrypt the information without the corresponding private key. Therefore, our
proposed scheme achieves data integrity property.

Moreover, we also compare our proposed ATMAS with its counterparts against
security requirements and authentication features in Table 1. From this table,
the work in [15]-[17] adopts crypto-based authentication schemes, in which the
schemes in [15] and [16] have no authentication at the user device side. The
work in [17] and our scheme use a two-phase authentication method, but the
second phase in [17] is still crypto-based, which is less robust than ours.

## Case Study and Performance Evaluations

In this section, we first provide the factors/features used in our analysis.
Then, a comparison of the performance of classic AI-based algorithms using
selected factors is presented. Lastly, we discuss the effect of factor
selection strategy on authentication accuracy.

In our case study, we consider three kinds of services: conversational,
streaming, and interactive. We assume that the BSs in the SAGIN are fixed and
uniformly distributed, and the coverage range of a BS is 20 $km$. The
satellites in the SAGIN orbit at a height of 20000 $km$ and have a maximum
beam scanning range of $\pm 11.64{{}^{\circ}}$. Table 2 provides the factors
used in our proposed ATMAS, including identities, trajectory, and
communication attributes.

Table 2: Descriptions of Factors in the ATMAS. Items | Factors
---|---
Factor 1 | Traffic Volume
Factor 2 | Service Type
Factor 3 | Uplink Rate
Factor 4 | Sinuosity of the MU
Factor 5 | Index of the BS
Factor 6 | Distance between the BS and the MU
Factor 7 | Position of the MU
Factor 8 | Heading azimuth of the MU
Factor 9 | Elevation between the BS and the Satellite

Figure 4: A Comparison of Authentication Accuracy with Classic AI-based
Algorithms.

Figure 5: Authentication Accuracy of the SAGIN Using a Different Number of
Factors.

Figure 6: Authentication Accuracy of the SAGIN Using Random Selected Factors.

We compare the authentication accuracy of different AI-based algorithms with
respect to the percentage of illegal access. The authentication accuracy is
defined as $ACC=\left(TP+TN\right)/\left(TP+FP+TN+FN\right)$, where $TP$,
$FP$, $TN$, and $FN$ denote true positives, false positives, true negative,
and false negative, respectively. As shown in Fig. 4, with an increase in the
percentage of illegal access, the authentication accuracy decreases slightly
by less than 8%. Besides, based on the random forest algorithm, the
authentication accuracy can achieve over 92%. By using the random forest
algorithm, the proposed authentication scheme is robust even if the percentage
of illegal access is high.

Fig. 5 illustrates the authentication accuracy using different numbers of
factors. F[1] means that we only choose Factor 1 and F[1-9] means that all
factors are selected. The results show that as the number of selected factors
increases, the authentication accuracy also increases. However, the increase
rate decreases, such that the authentication accuracy using 8 factors is
almost the same as that using 9 factors. This indicates that not every factor
has the same contribution to the increase in authentication accuracy.
Therefore, choosing the proper combination of factors makes lightweight and
effective authentication possible. In Fig. 6, we present the authentication
accuracy of a combination of 3 factors. The results show that the combination
with Factor 1, Factor 2 and Factor 4 has the highest authentication accuracy.
Besides, combinations with Factor 1 can achieve an average accuracy of more
than 95%, which indicates that in our simulation scenario, Factor 1
contributes the most to the ATMAS.

## CONCLUSION

In this article, an ATMAS is proposed in which spatial-temporal features are
taken into account, including traffic volume and geographic location. An AI-
based continuous authentication, referred to as ‘Phase II’, is performed to
intelligently grant or deny access reliably by capturing user profiles and
traits following a conventional cryptographic authentication named ‘Phase I’.
Security analysis is conducted to evaluate the robustness and validate the
security of the proposed Scheme. Performance evaluation shows that the mean
authentication accuracy achieves over $92\%$ by ATMAS. Additionally, we
analyze the importance of factors used in our case study, shedding light on
lightweight and efficient authentication mechanism design. However, there are
still several research opportunities for future study.

Authentication Centers Deployment. In our proposed ATMAS, authentication is
completed by the NCC on the ground. However, this increases transmission
delays since all authentication information must be transmitted to the NCC. To
address this, choosing MEO satellites as partial authentication centers is a
promising solution. However, communication and computation resources on the
satellite are limited, which requires optimization of the allocation of
authentication tasks between MEO satellites and the NCC.

Efficient AI-based Authentication. AI-based authentication algorithms have
intensive computation and communication costs and require a large amount of
training data as well as a complicated feature-extraction process [18].
Besides, low computation and communication overhead algorithms should be
investigated to the deployment of authentication centers on satellites.

Blockchain-based Intelligent Authentication. Blockchain is a distributed
ledger technology with characteristics of decentralization, security,
interoperation, and trust establishment [19]. As the SAGIN becomes
increasingly complex, the system may suffer from false authentications leading
to potential privacy leakage and security risks. Blockchain-based techniques
can be utilized to track past security breaches and provide the necessary log
analysis.

## ACKNOWLEDGMENTS

This work was supported in part by the National Key Research and Development
Program of China under Grant 2020YFB1804800 and Grant 2021YFB2900200, in part
by the Key Research and Development Program of Zhejiang Province under Grant
2021C01197, in part by the National Natural Science Foundation of China under
Grant 62101509, in part by the Natural Science Foundation of Zhejiang Province
under Grant LQ22F010018. The corresponding authors of this article are
Yongdong Zhu and Zipeng Li.

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Bin Yang received the Ph.D. degrees in information and communication
engineering from the Huazhong University of Science and Technology, Wuhan,
China, in 2018. His research interests include vehicular communication
networks, and reconfigurable intelligent surface assisted communications.
Contact him at<EMAIL_ADDRESS>
Shanyun Liu received the B.S., M.S., and Ph.D. degrees from the Department of
Electronic Engineering, Tsinghua University, Beijing, China, in 2014, 2016 and
2020, respectively. His current research interests include integrated
terrestrial-satellite communications, graph learning and information theory.
Contact him at<EMAIL_ADDRESS>
Tao Xu received the M.Sc. and Ph.D. degree in Aberystwyth University,
Abersytwyth, UK, in 2015 and 2021, respectively. He is currently a
postdoctoral researcher in Zhejiang Lab, Hangzhou, China. His current research
interests include evolutionary computation, constrained optimization, machine
learning, and graph computing. Contact him at<EMAIL_ADDRESS>
Chuyu Li received her B.E. and Ph.D. degrees from the University of
Wollongong, Australia, in 2017 and 2020, respectively. She joined Zhejiang
Lab, China in 2021 as a postdoctoral researcher. Her current research
interests include UAV-aided sensor networks, radar-radio systems and RF-energy
harvesting networks. Contact her at<EMAIL_ADDRESS>
Yongdong Zhu received his Ph.D. degree from the University of Essex,
Colchester, United Kingdom, in 2007. He is currently a professor with the
Zhejiang Lab. His current research interests include next generation mobile
communication, mobile edge network architecture, vehicular communication
networks, and Internet of Things. Contact him at<EMAIL_ADDRESS>
Zipeng Li received his Ph.D. degrees in information and communication
engineering from the Huazhong University of Science and Technology, Wuhan,
China, in 2021. He is currently a Lecturer with Nanchang University, Nanchang,
China. His research interests include vehicular networks, artificial
intelligence, and autonomous vehicles. Contact him at<EMAIL_ADDRESS>
Zhifeng Zhao received his Ph.D. degree in communication and information system
from the PLA University of Science and Technology, Nanjing, China, in 2002.
Currently, he is with Zhejiang Lab. His research area includes software
defined network, wireless network in 6G, computing network, and collective
intelligence. Contact him at<EMAIL_ADDRESS>


some superdeterminism or retrocausality. So in our view, these recent results
demonstrate that the common prejudice against the measurement problem as
unscientific and unsolvable is unfounded: the combination of these non-
absoluteness theorems, the need to avoid epistemic irrationality, and the
mandate to successfully reproduce the predictions of QFT is now forcing us
down an increasingly narrow bottleneck, so progress is certainly being made
and there is cause for optimism that we may eventually converge on a fully
acceptable solution.

##  10 Acknowledgements

Thanks to Nick Ormrod for reading and discussing a draft of this paper, and
thanks to Howard Wiseman and Yilé Ying for very helpful conversations on these
topics. This publication was made possible in part through the support of the
ID #62312 grant from the John Templeton Foundation, as part of the project
https://www.templeton.org/grant/the-quantum-information-structure-of-
spacetime-qiss-second-phase‘The Quantum Information Structure of Spacetime’
(QISS). The opinions expressed in this publication are those of the author and
do not necessarily reflect the views of the John Templeton Foundation.

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