Physical Layer Authentication Scheme Research Articles

Adaptive Physical-Layer Authentication for IoT in MIMO Communication Systems Using Support Vector Machine

Internet of things (IoT) devices have become ubiquitous due to the prevalence of the internet. However, the open nature of wireless networks makes them susceptible to spoofing attacks. Moreover, their heterogeneous characteristics create authentication challenges which are increasing due to the tremendous growth in the number and variety of devices. Physical layer authentication (PLA) provides a solution by utilizing the unique characteristics of wireless channels to aid upper layer authentication (ULA). In this paper, an adaptive physical layer authentication scheme is proposed which exploits the antenna diversity inherent in multi-input-multi-output (MIMO) systems. This scheme employs a one-class classification support vector machine (OCC-SVM) with the magnitude and real and imaginary parts of the received signals as features. Results are presented which show that this scheme provides robust authentication. The authentication performance is evaluated considering two majority voting schemes for IoT applications.

GALAMC: Guaranteed Authentication Level at Minimized Complexity Relying on Intelligent Collaboration

Conventional centralized authentication techniques based on both digital cryptography and physical-layer attributes are prone to single-point failure due to either compromised digital security keys or an abrupt change in the physical communication environment. Although these particular challenges could be mitigated by the joint use of decentralized authentication and physical-layer attributes, such schemes often exhibit unpredictable performance. Simultaneously, the necessary involvement of multiple parties and the imperfect observation of the physical communication environment can also significantly increase the latency and computational complexity. As a remedy, a decentralized authentication scheme is proposed in this paper to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guaranteed Authentication Level at Minimized Complexity</i> (GALAMC) based on the intelligent use of distributed collaboration and available distributive physical-layer attributes. Specifically, we aim for minimizing the complexity of the proposed collaborative authentication process by harnessing the minimum number of collaborative nodes and the selected authentication attributes at each node across the different environments while guaranteeing the required authentication level. The related physical-layer authentication scheme is implemented at each collaborative node where different physical-layer attributes can be selected based on their usefulness which is time-varying. The simulation results demonstrate that our scheme maintains the target level of authentication and it is more immune to sudden environmental changes than the conventional centralized physical-layer authentication scheme. It can also be observed that our proposed scheme can adaptively select the minimum number of collaborative nodes for adaptively minimizing the computational cost.

Rethinking Tag Collisions for Replay and Relay Attack Resistance in Backscatter Networks

Distinguishable physical layer features of radio frequency have the potential to serve as new fingerprints for authentication in backscatter networks. They have a definite advantage that backscatter tags do not have to run resource-intensive operations that commodity tags rarely implement. However, current physical layer authentication schemes impose substantial burdens on both service providers and users. Since physical layer features are highly susceptible to environmental factors, labor-intensive and time-consuming fingerprint library establishment is indispensable to make sufficient statistics for authentication. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TagDuet</i> , a collision-assisted authentication scheme that adopts an auxiliary tag to RFID systems, a typical type of backscatter networks, without the requirement of fingerprint library establishment. TagDuet places an independent backscatter tag in the proximity of a reader and utilizes the features in intended tag collisions to improve wireless security. Our phase cancellation decoding algorithm accurately decodes the collisions, which leads TagDuet fully compatible with the commodity RFID tags. TagDuet provides freshness, the paramount property to resist replay attacks, and robustness to relay attacks under FCC regulations for frequency hopping. We implement a prototype of TagDuet with commodity tags, evaluate the performance in randomized channels by the auxiliary tag, and demonstrate the resilience against replay and relay attacks.

Adaptive Physical Layer Authentication Using Machine Learning With Antenna Diversity

The heterogeneous characteristics of wireless mobile networks within the Internet of things (IoT) create authentication challenges due to the large number of devices with diverse requirements and capabilities. Physical layer authentication (PLA) can provide solutions for this heterogeneous environment using wireless channel attributes. In the paper, an adaptive lightweight physical layer authentication (ALPLA) scheme is proposed using machine learning (ML). Antenna diversity at the receiver is exploited to increase the number of features to achieve a high authentication rate (AR). A one-class classifier support vector machine (OCC-SVM) is used with the magnitude and real and imaginary parts of the received signal at each receive antenna as features. One-class classification is a ML technique for outlier and anomaly detection which uses only legitimate training data. The sounding reference signal (SRS) in the 5G uplink radio frame is employed to obtain the features. The proposed scheme is evaluated in an urban environment under different mobility conditions. Results are presented which show that this scheme provides a high AR with sufficient antenna diversity. Further it is superior to other approaches in the literature.

Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things

With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation–conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank–Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P f reduced to 0.0263% and the miss detection rate P m reduced to 0.3466%.

Online machine learning-based physical layer authentication for MmWave MIMO systems

Transmitter authentication is significantly critical to secure millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. By utilizing the mmWave MIMO channel and carrier frequency offset (CFO), this paper proposes a novel online machine learning-based physical layer authentication scheme to validate transmitter identities. In particular, with the help of the Gaussian kernel method and representer theorem, we formulate the physical layer authentication model as a binary classification model. Based on the developed classification model and a constructed convex objective function, we then design an online algorithm to achieve updating authentication parameters, and thus establish a novel online machine learning-based physical layer authentication scheme. Based on the developed authentication model and theories of statistical hypothesis testing, we statistically derive the analytical expressions for false alarm and detection rates for the proposed authentication scheme. Finally, we carry out extensive experiments to verify the authentication performance in terms of the false alarm and detection rates. In addition, we investigate the related authentication efficiency issues (e.g., convergence process) and the capability of resisting against spoofing attacks in mmWave MIMO systems.

Improved Physical Layer Authentication Scheme Based on Wireless Channel Phase

Aiming at the authentication problem in wireless communication, an improved physical layer authentication scheme based on wireless channel phase (PHY-IMACP) is proposed in this paper. In this scheme, legitimate nodes exploit the reciprocity of the channel phase response to conduct information exchange. The secret key information is hidden in the phase of the interactive information, and the authentication judgment is completed by use of binary hypothesis testing. Then the well-designed hypothesis testing statistic is proposed to obtain the better authentication performance. Additionally, mean and variance closed-form expressions of the hypothesis testing statistic in our proposed scheme are given. The simulation results show that the proposed scheme outperforms the benchmark at different conditions of eavesdropping channel.

BCAuth: Physical Layer Enhanced Authentication and Attack Tracing for Backscatter Communications

Backscatter communication (BC) enables ultra-low-power communications and allows devices to harvest energy simultaneously. But its practical deployment faces severe security threats caused by its nature of openness and broadcast. Authenticating backscatter devices (BDs) is treated as the first line of defense. However, complex cryptographic approaches are not desirable due to the limited computation capability of BDs. Existing physical layer authentication schemes cannot effectively support BD mobility, multiple attacker identification and attacker location tracing in an integrated way. To tackle these problems, this paper proposes BCAuth, a multi-stage authentication and attack tracing scheme based on the physical spatial information of BDs to realize enhanced BD authentication security for both static and mobile BDs. After initial authentication based on BD identity with its position information registration, preemptive authentication and re-authentication are performed according to spatial correlation of backscattered signal source locations associated with the BD. By exploiting clustering-based analysis on spacial information, BCAuth is capable of determining the number of attackers and localizing their positions. In addition, we propose a reciprocal channel-based method for BD re-authentication with better authentication performance than the clustering-based method for mobile BDs when the BDs is able to measure received signal strength (RSS), which also enables mutual authentication. We theoretically analyze BCAuth security and conduct extensive numerical simulations with various settings to show its desirable performance.

Multiuser physical layer security mechanism in the wireless communication system of the IIOT

Wireless system in industrial scene plays an important role in the process of automation. This kind of system urgently needs low complexity, lightweight, high security authentication mechanism. The emergence of physical layer authentication meets these requirements. However, the existing authentication mechanism based on binary hypothesis testing can only perform ideally under fixed conditions, and cannot distinguish multiple users; The authentication mechanism based on deep neural network (DNN) algorithm has limitations in small sample learning and parameter setting. In order to further improve the accuracy of authentication in dynamic industrial scenarios, a new multiuser physical layer authentication scheme is proposed. The mechanism uses machine learning algorithm based on autonomous parameter optimization to replace the traditional decision making method based on user-defined threshold, and is suitable for small sample learning. This paper takes the channel matrix estimated by the mobile node as the authentication input, obtains different channel matrix dimensions through down sampling, and finds out the optimal channel matrix dimension through experiments, so as to reduce the running time and improve the authentication accuracy. A large number of simulations are carried out using the public dynamic industrial scene data set. Compared with the existing authentication schemes, the proposed authentication scheme further improves the accuracy of multiuser authentication in dynamic industrial scenarios.

Physical-Layer Authentication in Wirelessly Powered Communication Networks

This paper addresses the problem of authenticating the transmitter device in wirelessly powered communications networks (WPCNs). We proposed a physical-layer authentication scheme for a WPCN. In comparison with upper-layer authentication schemes, the proposed scheme has low complexity, low power consumption, low overhead, and high security. For comprehensively analyzing the performance of the proposed scheme by considering the requirements of transmission delay, security, and reliability together, we put forward an analytical framework by considering the probabilities of transmission, security outage, connection outage, and joint security-connection outage. Based on the proposed analytical framework, we further introduced a new systematic metric by calculating the achievable throughput of all users with considering the performance of transmission-delay, security, and reliability together. We defined this new systematic metric as the overall transmission efficiency (OTE) of a WPCN, which can effectively quantize the average efficiency of message transmission in the WPCN with physical layer authentication. We analyzed the events represented by these probability factors over random fading channels and explicitly derive their closed-form expressions. For defending against jamming attacks, we further proposed another metric to estimate which user has the high probability of suffering from jamming attacks. The new metric represents that the adversary has the largest attacking gain with the minimum cost. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the proposed scheme accurately detects an impersonating attack and drops its contribution to the sum of long-term throughput.

Worst-Case Detection Performance for Distributed SIMO Physical Layer Authentication

Feature-based physical layer authentication (PLA) schemes, using position-specific channel characteristics as identifying features, can provide lightweight protection against impersonation attacks in overhead-limited applications like e.g., mission-critical and low-latency scenarios. However, with PLA-aware attack strategies, an attacker can maximize the probability of successfully impersonating the legitimate devices. In this paper, we provide worst-case detection performance bounds under such strategies for a distributed PLA scheme that is based on the channel-state information (CSI) observed at multiple distributed remote radio-heads. This distributed setup exploits the multiple-channel diversity for enhanced detection performance and mimics distributed antenna architectures considered for 4G and 5G radio access networks. We consider (i) a power manipulation attack, in which a single-antenna attacker adopts optimal transmit power and phase; and (ii) an optimal spatial position attack. Interestingly, our results show that the attacker can achieve close-to-optimal success probability with only statistical CSI, which significantly strengthens the relevance of our results for practical scenarios. Furthermore, our results show that, by distributing antennas to multiple radio-heads, the worst-case missed detection probability can be reduced by 4 orders of magnitude without increasing the total number of antennas, illustrating the superiority of distributed PLA over a co-located antenna setup.

Privacy Preserving Physical Layer Authentication Scheme for LBS based Wireless Networks

With the fast development in services related to localisation, location-based service (LBS) gains more importance amongst all the mobile wireless services. To avail the service in the LBS system, information about the location and identity of the user has to be provided to the service provider. The service provider authenticates the user based on their identity and location before providing services. In general, sharing location information and preserving the user’s privacy is a highly challenging task in conventional authentication techniques. To resolve these challenges in authenticating the users, retaining users’ privacy, a new SVD (singular value decomposition) based Privacy Preserved Location Authentication Scheme (SPPLAS) has been proposed. In this proposed method, physical layer signatures such as channel state information (CSI) and carrier frequency offset (CFO) are used for generating secret key required for encrypting the user’s location and identity information, and thus encrypted user’s information is sent to service provider for authentication. Secret key is generated by applying SVD on CSI vector. The proposed scheme aids in authenticating the user through location information while protecting the user’s privacy. The performance of the proposed method is evaluated in terms of bit mismatch, leakage and bit error rate performance of receiver and adversary. The simulation results show that the proposed scheme achieves better robustness and security than the existing location-based authentication techniques.

Safeguarding Cluster Heads in UAV Swarm Using Edge Intelligence: Linear Discriminant Analysis-Based Cross-Layer Authentication

While the unmanned aerial vehicles (UAVs) swarm travels under a dynamic environment, the cluster head (CH) switching is unavoidable due to the mitigation of mobility, quality of service, and energy consumption. If an attacker becomes the new CH, the entire swarm will be controlled and the sensitive data will be leaked. Unlike the other mobile networks with constant network connectivity, the authentication in the UAV swarm suffers from intermittent connection with the ground station under a hostile environment or spectrum constraint condition. Hence, this paper proposes a novel CH safeguarding mechanism enabled by edge intelligence utilizing a situational-aware authentication scheme. This low-latency mechanism provides extra security at the CH selection and switching without cloud server support. By adopting the unique cross-layer attributes, the system security is significantly improved based on the extracted multi-dimensional information. The Linear Discriminant Analysis (LDA) algorithm fuses the authentication decision accurately by projecting the high dimensional estimations into a low dimensional space for maximum separability by only keeping the necessary attributes. A situation-aware cross-layer attribute selection algorithm is developed to select a minimum number of attributes so that the time required for attribute estimation and computation overhead of authentication can be reduced. The simulation results demonstrate that our scheme performs better under a dynamic environment compared with the physical layer authentication scheme and some existing state-of-the-art authentication techniques.

Physical layer authentication for automotive cyber physical systems based on modified HB protocol

Automotive cyber physical systems (CPSs) are ever more utilizing wireless technology for V2X communication as a potential way out for challenges regarding collision detection, wire strap up troubles and collision avoidance. However, security is constrained as a result of the energy and performance limitations of modern wireless systems. Accordingly, the need for efficient secret key generation and management mechanism for secured communication among computationally weak wireless devices has motivated the introduction of new authentication protocols. Recently, there has been a great interest in physical layer based secret key generation schemes by utilizing channel reciprocity. Consequently, it is observed that the sequence generated by two communicating parties contain mismatched bits which need to be reconciled by exchanging information over a public channel. This can be an immense security threat as it may let an adversary attain and recover segments of the key in known channel conditions. We proposed Hopper-Blum based physical layer (HB-PL) authentication scheme in which an enhanced physical layer key generation method integrates the Hopper-Blum (HB) authentication protocol. The information collected from the shared channel is used as secret keys for the HB protocol and the mismatched bits are used as the induced noise for learning parity with noise (LPN) problem. The proposed scheme aims to provide a way out for bit reconciliation process without leakage of information over a public channel. Moreover, HB protocol is computationally efficient and simple which helps to reduce the number of exchange messages during the authentication process. We have performed several experiments which show that our proposed design can generate secret keys with improved security strength and high performance in comparison to the current authentication techniques. Our scheme requires less than 55 exchange messages to achieve more than 95% of correct authentication.

Fuzzy Learning for Multi-Dimensional Adaptive Physical Layer Authentication: A Compact and Robust Approach

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

Physical-layer authentication based on adaptive Kalman filter for V2X communication

Security and authentication are an important guarantee for vehicle to everything (V2X). At present, V2X security schemes in most countries are based on public key infrastructure (PKI). These schemes require additional digital signatures when sending basic safety messages (BSMs), which means security requires additional overhead. Consequently, V2X communication efficiency will be affected in the case of traffic congestion. To solve this problem, we propose a physical-layer authentication model based on channel characteristics in the V2X environment. The model uses the Sage-Husa adaptive Kalman filter, which can dynamically adjust statistical properties of the noises according to observation. Furthermore, we select CSI and RSSI to design a physical-layer authentication scheme based on this model, which can replace the digital signature. Finally, we analyze the impact of the filter algorithm and the factors affecting the authentication threshold, and compare our work with similar schemes. As a result, the proposed scheme makes minor changes to the PKI-based V2X security and effectively reduces the overhead of security. Due to the time-varying channel characteristics, this scheme can prevent impersonation by malicious attackers, thereby improving the security of communication.

Physical-Layer Authentication in Non-Orthogonal Multiple Access Systems

This paper concerns the problem of authenticating the transmitter device in non-orthogonal multiple access (NOMA) systems. This problem is important because of high vulnerabilities in wireless communications and an additional security vulnerability when some users collude with the adversary. In this paper, we consider two attacking scenarios. In the first scenario, there is no user that colludes with the adversary. In the second scenario, at least one user colludes with the adversary. In this paper, we propose an authentication approach in the down-link scheme of a NOMA system using physical-layer authentication mechanism because of its advantages: provide information theoretic security, reduce complexity, save power and allow us to construct a two-factor authentication system. Based on the aforementioned two attacking scenarios, we propose three physical-layer authentication schemes for a NOMA system: Physical-Layer Authentication with Shared Authentication Tag (PLA-SAT), Physical-Layer Authentication with Superimposed Independent authentication Tags (PLA-SIT), and Physical-Layer Authentication with TDM authentication tags (PLA-TDM). We analyze the theoretical performance of our schemes over fading channels and derive their closed-form expressions. Then, we optimize the parameters of our schemes to achieve both the reliability fairness and the authentication-accuracy fairness. We implemented our schemes and conducted extensive performance comparisons. Our experimental results show that for the first attacking scenario, the PLA-SAT scheme offers more than 97% authentication accuracy when the received SNR of the served user with poor channel condition is at least 10 dB. For the second attacking scenario, both the PLA-SIT scheme and the PLA-TDM scheme offer more than 97% authentication accuracy when the received SNR of the served user with poor channel condition is at least 12 dB.

Physical Layer Authentication Jointly Utilizing Channel and Phase Noise in MIMO Systems

In this paper, we propose a physical layer authentication scheme in heterogeneous coexist multiple-input-multiple-output (MIMO) systems. This scheme utilizes two physical layer features in terms of location-specific channel gains and transmitter-specific phase noise caused by imperfect oscillators to identify transmitters. Three properties of the proposed scheme: covertness, robustness, and security, are analyzed in detail. By using a maximum-likelihood estimator (MLE) and extended Kalman filter (EKF), we estimate channel gains and phase noise, and formulate variances of estimation errors. We also quantize the temporal variations of channel gains and phase noise through the developed quantizers. Based on quantization results and theories of hypothesis testing and stochastic process, we then derive the closed-form expressions for false alarm and detection probabilities with the consideration of quantization errors. Simulations are carried out to validate the theoretical results of the two probabilities. Based on theoretical models, we further demonstrate that the proposed scheme makes it possible for us to flexibly control authentication performance by adjusting thresholds (for channel gain, phase noise, and decision, respectively) to achieve a required authentication performance in specific MIMO applications.

Physical Layer Authentication Based on Nonlinear Kalman Filter for V2X Communication

Authentication is an important guarantee for vehicle to everything (V2X) commercial deployment. Currently, V2X security often use identity authentication schemes based on public key infrastructure (PKI). These schemes need to transmit certificates and signatures when sending safety-related information such as basic safety messages (BSMs), which need to occupy extra bandwidth and reduce the available channel capacity. So, V2X communication efficiency will be seriously affected in traffic congestion. In this paper, we propose a V2X authentication model based on physical layer characteristics. Then we use the Kalman filter to refine the iterative model and threshold model. The iterative model mainly realizes the priori and posteriori estimation of the current time based on the physical layer characteristics of the previous time, which provides the basis for the entire authentication process. The threshold model analyzes the mathematical characteristics of the priori estimation, and gives the calculation method of the authentication threshold. Since the conventional Kalman filter can only be used for linear discrete system, we use extended Kalman filter and unscented Kalman filter to extend the characteristics used for authentication to non-linearity. At the same time, iterative model and threshold model are improved according to these two algorithms. In terms of security and performance, we compare the proposed schemes with the conventional V2X authentication scheme and physical layer authentication scheme, and the effects of these schemes are analyzed by experiment. We select three characteristics for simulation: received signal strength indication (RSSI), the distance between the two vehicles, and the relative speed between the two vehicles. Then we analyze the process and effect of these two filters, and the factors that affect the threshold. Through experiments, the proposed authentication schemes can effectively take the responsibility of identity authentication in the V2X environment, and have high security level and low overhead, which can reduce the consumption of communication resources by security.

Physical layer authentication scheme with channel based tag padding sequence

This study proposes a novel physical layer authentication scheme, which uses the channel to generate a tagged signal and determine random partial padding. The scheme utilises the random wireless channel gain between communication entities to generate tag and decide a padding sequence for each symbol. Simulations for different padding percentages using the randomness provided by the channel are carried out to compare against a traditional full padding scheme, which shows improvement in error rate under the same security performance. The second layer of referee verification is introduced to increase the difficulty of compromising the authenticity of the scheme. A mathematical analysis is carried out to find an optimal size of selected time slots for the authentication scheme is also discussed.