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Competing service providers in the cloud environment ensure services are delivered under the promised security requirements. It is crucial for mobile services where user's movement results in the service's migration between edge servers or clouds in the Continuum. Maintaining service sovereignty before, during, and after the migration is a real challenge, especially when the service provider has committed to ensuring its quality following the Service Level Agreement. In this paper, we present the main challenges mobile service providers face in a cloud environment to guarantee the required level of security and digital sovereignty as described in the Security Service Level Agreement, with emphasis on challenges resulting from the service migration between the old and new locations. We present the security and sovereignty context intended for migration and the steps of the migration algorithm. We also analyze three specific service migration cases for three vertical industries with different service quality requirements. [ABSTRACT FROM AUTHOR]

Autonomous systems (ASs) often cooperate to ensure safe navigation. Embedded within the centralized or distributed coordination mechanisms are a set of observations, unobservable states, and control variables. Security of data transfer between ASs is crucial for safety, and both cryptography and physical layer security (PLS) methods have been used to secure communication surfaces—each with its drawbacks and dependencies. [ABSTRACT FROM AUTHOR]

For wireless communication systems with large-scale multiple-input–multiple-output array in the presence of an active eavesdropper, the eavesdropper masquerades as a legitimate user sending the identical pilot sequence to manipulate channel estimation in the uplink training phase, which misleads precoding at the transmitter. If such pilot spoofing attack is not handled effectively, it will not only reduce system capacity, but also cause much signal power leak to the spoofer, and so it seriously threatens the security transmission in wireless communication. Nevertheless, how to detect such potential pilot spoofing and recover the contaminated channel plays a significant role in designing combating schemes and ensuring security transmission. In this paper, the impact of pilot spoofing attack on the system security capacity is investigated, and it is shown that pilot spoofing attack will degrade system capacity greatly and the transmitted information will be intercepted by the spoofer. Then, a novel detection scheme based on pilot-random-block-encryption is proposed, through which not only the detection probability on the potential pilot spoofing can be improved greatly, but also the channel state information of the legal user and the spoofer are obtained. Simulation results show that the detection probability of the proposed detection scheme reaches 93% even the transmit power of pilot spoofing is as low as − 10 dB, which is much greater than other existing schemes, and the system security capacity is also enhanced with the proposed channel estimation strategy. [ABSTRACT FROM AUTHOR]

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), as a revolutionary technique, can boost transmission security by controlling unfavorable environments for signal eavesdropping and reducing interference. Integrating unmanned aerial vehicles (UAVs) with STAR-RISs has generated considerable interest due to its enhanced deployment flexibility. However, developing secure communication capabilities using flying STAR-RIS remains an open issue. Therefore, this work investigates the secrecy energy efficiency (SEE) maximization problem for the uplink non-orthogonal multiple access (NOMA) systems, where the UAV-mounted STAR-RIS is employed against the eavesdroppers. Specifically, we consider the joint optimization of the power control, the transmission/reflection coefficients, and the UAV/STAR-RIS’s placement for static and mobile scenarios. The problems are also subject to the minimum data rate requirements and the safety flight region. To tackle the intractable problems, we first adopt the iterative-based method to solve the problem under the static scenario. After that, we invoke the fractional programming and successive convex approximation methods to get the power control scheme, the semidefinite relaxation method to get the transmission/reflection (T/R) coefficients design, and the search-based method to obtain the UAV/STAR-RIS position. Extending to the mobile scenario, we adopt the double deep Q-network (DDQN) algorithm to learn the online UAV trajectory design policy from a long-term perspective. Numerical results unveil that: 1) the proposed iterative-based joint optimization algorithm for static scenarios achieves a near-optimal solution; 2) the NOMA communications aided by the UAV-mounted STAR-RIS achieve significant SEE gain over the conventional reflection-only RIS and the fixed STAR-RIS cases; 3) the DDQN-based algorithm for mobile scenario achieves a near-optimal solution and obtains a valuable performance gain over the short-sighted greedy algorithm. [ABSTRACT FROM AUTHOR]

Zero-day vulnerabilities are undisclosed security flaws exploited by attackers to gain unauthorized access to vulnerable systems. Firewalls and antivirus software are essential for preventing intrusions. While firewalls monitor network traffic to prevent unauthorized access, they may struggle to filter all incoming traffic due to attacker-generated anomalies. We present a novel approach to automate the generation of dynamic firewall rules, aiming to address this challenge through the utilization of a custom software solution in conjunction with a suitable programming language. This approach autonomously maintains and updates firewall rules, bolstering network security and streamlining the identification and mitigation of zero-day vulnerabilities. We evaluated the approach on a simulated network, demonstrating its efficacy in detecting and preventing unauthorized access through zero-day attacks. Our automated method for dynamic firewall rule creation offers an efficient means to address zero-day vulnerabilities. It empowers businesses to safeguard against cyberattacks and protect sensitive data. With adaptability to changing network requirements, it is valuable for organizations of different scales. In conclusion, our proposed automated approach for dynamic firewall rule generation provides a scientifically validated solution to address zero-day vulnerabilities. It enhances security measures, defends against emerging threats, and enables businesses to protect their systems and data effectively.

Emergency logistics is of great significance for supply security in emergencies. As a crucial component of strategic material reserve and allocation, emergency logistics is characterized by high levels of safety and efficacy. The security of the IoT-based emergency logistics system cannot be overstated, considering the potential damage caused by malicious accessors. Authentication is an essential means of system security. Existing certificateless authentication protocols are mostly based on bilinear pairings, which cannot match the objectives of emergency logistics networks for lightweight deployment and fast authentication of massive nodes. In this paper, we propose a lightweight certificateless authentication protocol (CL-LAP) without bilinear pairings. The security is determined by the discrete logarithm problem on elliptic curves. Nonlinear pairings guarantee energy efficiency and minimum computation cost. In addition, we use batch verification to reduce the authentication cost and tackle the problem of quick authentication in broadcast messages. We conduct a security analysis on the proposed CL-LAP under the random oracle model to demonstrate that the proposed scheme can withstand common security threats and provide the necessary security for the perception layer. The performance analysis indicates that the suggested scheme is less complex and more efficient than similar schemes with the same level of security.

Critical infrastructure (CI), such as energy and water distribution systems, is essential for the stability and well-being of the modern society. Industrial automation and control systems (IACSs) form the backbone of CIs and enable the operation of such systems in a safe and reliable manner. However, with the increasing use of industrial Ethernet communication protocols, such as Modbus-over-TCP (Modbus/TCP), once air-gapped IACSs are becoming vulnerable to potential cybersecurity threats. This paper presents a novel method for enhancing the cybersecurity of Modbus/TCP-based IACSs by implementing an authentication method based on message authentication codes (MACs). To provide partial protection of communication even when communicating with legacy Modbus/TCP peers, we propose a novel supervising device that analyzes exchanged messages and verifies the authenticity of the protected messages. To experimentally verify the protection method, a water-treatment cyber-physical system (CPS) was implemented as a digital twin in a programmable logic controller (PLC). The underlying MAC is the Chaskey-12, lightweight MAC defined in IEC 29192-6. It was implemented in the PLC program using the programming languages defined in IEC 61131-3. As an additional contribution, the presented implementation allows protection of communication between PLCs and other Modbus/TCP peers installed in existing IACSs without hardware or firmware modifications. The results show that the method provides protection against network attacks without significantly affecting performance, also demonstrating the feasibility of such protection in IACSs.

We introduce an encryption device based on a compact wave-chaotic cavity to enhance the physical security of wireless communication. The proposed encryption device is composed of a compact quasi-2D disordered cavity, where transmit signals pass through to be distorted in time before transmission. On the receiving end, the signals can only be decrypted when they pass through an identical cavity. In the absence of a proper decryption device, the signals cannot be properly decrypted. If a cavity with a different shape is used on the receiving end, vastly different wave propagation paths will prevent the signals from being decrypted, causing them to appear as noise. We experimentally demonstrate the proposed concept in an apparatus representing a wireless link.

We propose a method by which two radio frequency (RF) communication terminals exchange encryption keys or other data securely. This method draws on the approach developed for quantum key distribution (QKD) for detecting eavesdroppers but our method does not use any quantum properties at all. Instead, by exploiting the effects an eavesdropper has on channel stability, we explore a line-of-sight link radio in which data transfer rates are so high as to approach the Shannon limit. With very steep rises in bit error rate accompanying a small degradation of signal-to-noise limits for certain forward error correction codes, it becomes possible to infer the existence of an eavesdropper before they are able to obtain a complete key. We describe our method and analyse one possible implementation using low density parity check codes with quadrature phase shift keying modulation. The proposed technique is in principle far easier to implement than quantum-based approaches for RF and optical wireless links since the required hardware is readily available and the basic principles are well known and well understood. Finally, we show our method to have a higher key rate and spectral efficiency than those of QKD.

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search. [ABSTRACT FROM AUTHOR]

In this work, we mainly focus on low probability detection (LPD) and low probability interception (LPI) wireless communication in cyber-physical systems. An LPD signal waveform based on multi-carrier modulation and an under-sampling method for signal detection is introduced. The application of the proposed LPD signal for physical layer security is discussed in a typical wireless-tap channel model, which consists of a transmitter (Alice), an intended receiver (Bob), and an eavesdropper (Eve). Since the under-sampling method at Bob's end depends very sensitively on accurate sampling clock and channel state information (CSI), which can hardly be obtained by Eve, the security transmission is initialized as Bob transmits a pilot for Alice to perform channel sounding and clock synchronization by invoking the channel reciprocal principle. Then, Alice sends a multi-carrier information-bearing signal constructed according to Bob's actual sampling clock and the CSI between the two. Consequently, Bob can coherently combine the sub-band signals after sampling, while Eve can only obtain a destructive combination. Finally, we derived the closed-form expressions of detection probability at Bob's and Eve's ends when the energy detector is employed. Simulation results show that the bit error rate (BER) at Alice's end is gradually decreased with the increase in the signal-to-noise ratio (SNR) in both the AWGN and fading channels. Meanwhile, the BER at Eve's end is always unacceptably high no matter how the SNR changes. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks are among the most severe threats in cyberspace. The existing methods are only designed to decide whether certain types of DDoS attacks are ongoing. As a result, they cannot detect other types of attacks, not to mention the even more challenging mixed DDoS attacks. In this paper, we comprehensively analyzed the characteristics of various types of DDoS attacks and innovatively proposed five new features from heterogeneous packets including entropy rate of IP source flow, entropy rate of flow, entropy of packet size, entropy rate of packet size, and number of ICMP destination unreachable packet to detect not only various types of DDoS attacks, but also the mixture of them. The experimental results show that the proposed fives features ranked at the top compared with other common features in terms of effectiveness. Besides, by using these features, our proposed framework outperforms the existing methods when detecting various DDoS attacks and mixed DDoS attacks. The detection accuracy improvements over the existing methods are between 21% and 53%. [ABSTRACT FROM AUTHOR]

Owing to network function virtualization (NFV), each industrial application is constructed as a service function chain (SFC), concatenating the ordered service functions, to offer applications more flexibly in industrial Internet of Things (IIoT). When it comes to the emerging edge intelligence, the integration of NFV with edge in IIoT would enable more close-proximity services, yet also posing new challenges owing to more complicated environment. Although some efforts have been made to service function chaining in IIoT, the radio resource dynamics are not fully perceived. In this article, we investigate the radio-aware SFC deployment in the edge-enabled IIoT. First, a radio-aware deployment formulation is exhibited, steering the flow traversing both wireless and wired links. Next, Markov decision process is exhibited to track dynamics in both IIoT and radio resources. Afterwards, the natural gradient-based actor-critic SFC paradigm is introduced to adapt to network variation, by incorporating the curvature of parameter space into gradient information. To resolve the high-dimensionality in action space, we then recur to the norm penalty approach, reducing the space size by two orders of magnitude. Finally, numerical experiments are executed to uncover superiority of presented method, disclosing that the latency performance benefits from both the SFC routing between IIoT servers and elaborated wireless resource orchestration. [ABSTRACT FROM AUTHOR]

Rogue Base Stations (RBS), also known as 5G Subscription Concealed Identifier (SUCI) catchers, were initially developed to maliciously intercept subscribers' identities. Since then, further advances have been made, not only in RBSs, but also in communication network security. The identification and prevention of RBSs in Fifth Generation (5G) networks are among the main security challenges for users and network infrastructure. The security architecture group in 3GPP clarified that the radio configuration information received from user equipment could contain fingerprints of the RBS. This information is periodically included in the measurement report generated by the user equipment to report location information and Received Signal Strength (RSS) measurements for the strongest base stations. The motivation in this work, then is to generate 5G measurement reports to provide a large and realistic dataset of radio information and RSS measurements for an autonomous vehicle driving along various sections of a road. These simulated measurement reports can then be used to develop and test new methods for identifying an RBS and taking mitigating actions. The proposed approach can generate 20 min of synthetic drive test data in 15 s, which is 80 times faster than real time. [ABSTRACT FROM AUTHOR]

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is changing every technology we are used to deal with. Autonomy has long been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Big auto manufacturers as well are investing billions of dollars to produce Autonomous Vehicles (AVs). This new technology has the potential to provide more safety for passengers, less crowded roads, congestion alleviation, optimized traffic, fuel-saving, less pollution as well as enhanced travel experience among other benefits. But this new paradigm shift comes with newly introduced privacy issues and security concerns. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected. They collect huge troves of information, which needs to be protected from breaches. In this work, we investigate security challenges and privacy concerns in AVs. We examine different attacks launched in a layer-based approach. We conceptualize the architecture of AVs in a four-layered model. Then, we survey security and privacy attacks and some of the most promising countermeasures to tackle them. Our goal is to shed light on the open research challenges in the area of AVs as well as offer directions for future research.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS. [ABSTRACT FROM AUTHOR]

Short packet communications play key roles in the Internet-of-Things. Conventional Shannon’s coding theorem is not applicable for short packet communications, and the achievable rate in the finite blocklength regime is related to the blocklength and the decoding error probability. Contrary to conventional physical layer security, wireless information surveillance assumes that the communication users are suspicious users and the eavesdroppers are legitimate monitors, and proactive eavesdropping via jamming has been proposed to improve the eavesdropping performance. Existing works on proactive eavesdropping ignored the scenario when the suspicious users adopt the short packet communications. Thus, this paper tries to develop effective proactive eavesdropping schemes suitable for the short packet suspicious communications. Under the truncated channel inversion power allocation and the modified constant power allocation policies at the suspicious source, the problems of jamming power allocation for maximizing the monitoring success probability subject to the average transmit power constraint at the monitor are investigated. Based on the Dinkelbach-type algorithm, the Lagrange duality method and a novel tractable approximate expression for the decoding error probability, we derive suboptimal solutions to the problems. Simulation results demonstrate the effectiveness of the proposed solutions compared to various benchmark algorithms in existing literature. [ABSTRACT FROM AUTHOR]

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme. [ABSTRACT FROM AUTHOR]

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is considered to be a backbone of smart transportation system where vehicles are expected to exchange traffic information and other data using vehicle-to-vehicle (V2V) communications. Furthermore, V2V communications is regarded as a medium to send upcoming traffic information to vehicles/drivers or passengers using wireless technology in a timely manner. This will help reduce traffic accidents, traffic jams, fuel consumption caused by traffic jams, loss of hours caused by delayed commuting and carbon emission caused by vehicles. Because of the critical nature of IoV for smart transportation systems, secure wireless communication is one of the critical and essential components to offer trustworthy services through IoV. Like in other wireless systems, wireless communication is open in air, which makes attackers’ job easy to launch the malicious actions in IoV. In this paper, we investigate the impact of mobility of vehicles for secure communications in IoV with joint jamming and eavesdropping attacks. We investigate different scenarios, when vehicles (legitimate ones and attackers) travel in same and opposite directions with low speed (school zone speed) limit to highway speed limit. Furthermore, we study the impact of association time and encryption time not only for establishing the connection between vehicles for V2V communications but also for exchanging messages for vehicles after successful association between vehicles and encryption of the message. In addition, we also study the joint impact of jamming and eavesdropping attacks in IoV for different speed limits, transmission ranges and wireless bandwidths. We investigate the extra cost in terms of transmit power and bandwidth incurred by jamming and eavesdropping attacks while maintaining the same secrecy rate as it could have been without those attacks. Numerical results are used to support the analysis. [ABSTRACT FROM AUTHOR]

Modern and future services require ultra-reliable mobile connections with high bandwidth parameters and proper security protection. It is possible to ensure such conditions by provisioning services in the Multi-Access Edge Computing system integrated with fifth-generation mobile networks. However, the main challenge in the mentioned architecture is providing a secure service migration with all related data and security requirements to another edge computing host area when the user changes its physical location. This article aims to present the state of research on the migration of the security context between service instances in Edge/MEC servers, specify steps of the migration procedure, and identify new security challenges inspired by use cases of 5G vertical industries. For this purpose, the analysis of the security context's structure and basic concept of the Security Service Level Agreement was performed and presented in the document. Next, a further investigation of the security context was conducted, including requirements for its reliable migration between edge serves instances. The study mainly focused on crucial migration challenges and possible solutions to resolve them. Finally, the authors presented how the proposed solution can be used to protect 5G vertical industries services based on several mobile use cases. [ABSTRACT FROM AUTHOR]

The current hot topic in cyber-security is not constrained to software layers. As attacks on electronic circuits have become more usual and dangerous, hardening digital System-on-Chips has become crucial. This article presents a novel electronic core to encrypt and decrypt data between two digital modules through an Advanced eXtensible Interface (AXI) connection. The core is compatible with AXI and is based on a Trivium stream cipher. Its implementation has been tested on a Zynq platform. The core prevents unauthorized data extraction by encrypting data on the fly. In addition, it takes up a small area—242 LUTs—and, as the core's AXI to AXI path is fully combinational, it does not interfere with the system's overall performance, with a maximum AXI clock frequency of 175 MHz. [ABSTRACT FROM AUTHOR]

Recently, the problem of security distributed estimation in adversarial multitask wireless sensor networks has attracted extensive attention. For example, malicious attackers always affect signal processing and reduce network estimation performance by destroying data information. To address this issue, by using task similarity, this article proposes a diffusion least-mean square algorithm based on Bayes (BDLMS) in multitask adversarial network environment. The BDLMS algorithm can divide the wireless sensor system into two subsystems: Noncooperative LMS (NCLMS) subsystem and distributed diffusion LMS (DLMS) subsystem. Among them, the NCLMS subsystem ensures the state consistency of neighboring nodes by task similarity and calculates the posterior probability of node being attacked by Bayes rule to achieve attack detection. The DLMS subsystem performs parameter estimation by combining safe and reliable information. In addition, the mean and mean-square stability of the BDLMS algorithm are analyzed. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments in different network environments. [ABSTRACT FROM AUTHOR]

This paper investigates side-channel vulnerabilities of a wireless communication application in vehicular environments (DSRC/WAVE) protocol implementation of a traffic intersection application. A prototype roadside unit (RSU) was implemented using real DSRC devices. The functionality of the WAVE short message (Wsm)-channel is extended to include an implementation of WAVE short message protocol (WSMP) for broadcasting GPS data and RSU instructions in vehicular communications. In the example used, DSRC is used to replace an intersection stoplight. Denial of service attacks are executed that leverage DSRC RSU timing and packet size side-channels to selectively disable the stoplight. Simulations are implemented to determine our ability to stealthily drop packets so as to force two vehicles to collide. Hidden Markov models (HMM) and Support Vector Machines (SVM) are constructed from sniffed side-channel information. We use inter-packet delay time and packet size side-channel information to design our attackes. In operational networks, packets should be encrypted in order to hide the contents of the packet payloads, but packet sizes and timing are not affected by encryption. HMMs were inferred using only side-channel information. The inferred HMMs track the protocol status over time. The SVM classifier was inferred using both side-channel data and packet payloads. At run-time, though, the SVM only had access to side-channel information. Simulation experiments were implemented to test HMM and SVM ability to identify packets used to signal vehicles to stop and yield right-of-way. Timing HMM side-channel attack caused collision with 2.5% false positive rate (FPR), while the packet size one resulted 9.5% FPR. [ABSTRACT FROM AUTHOR]

The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0. [ABSTRACT FROM AUTHOR]

This paper studies a secure multiuser multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS), where multiple colluding eavesdroppers (EVEs) coexist. We aim to maximize the sum secrecy rate (SSR) via jointly optimizing the beamforming vectors, the artificial noise (AN) and the phase shifts at the IRS subject to the maximum transmit power constraint and unit modulus constraints. To address the non-convex optimization problem, we first propose an alternating optimization (AO) algorithm based on semidefinite relaxation (SDR) and obtain a high-quality sub-optimal solution. In order to reduce the high computational complexity, a low-complexity alternating optimization (LC-AO) algorithm is developed, in which the beamforming vectors, AN and the IRS phase shifts are optimized alternately by the generalized power iteration (GPI) and the Riemannian manifold conjugate gradient (RMCG) algorithm, respectively. Simulation results show the advantages of deploying the IRS in improving the system secrecy performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility and condition of exploiting wireless-powered signals as a cover for conducting covert communications in unmanned aerial vehicle (UAV) networks. Specifically, we aim to design the hovering altitude of a UAV and its prior transmission probability to strike a balance between energy replenishment and covert communications. We derive the necessary condition for guaranteeing communication covertness subject to an energy replenishment constraint, under which the UAV’s optimal hovering and the prior transmission probability are determined. Our simulation shows that varying the prior transmission probability can significantly improve the achievable communication covertness under the energy replenishment constraint, relative to a baseline scheme with a fixed prior transmission probability. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization. [ABSTRACT FROM AUTHOR]

The nature of quantum mechanics provides us with an opportunity to statistically detect eavesdropping in quantum key distribution (QKD) protocols, which is unimaginable in classical digital communications. By utilizing Hoeffding’s inequality, this study analyzes the upper bounds of the false-positive ratio (FPR) and false-negative ratio (FNR) of eavesdropping detection in the Bennett–Brassard-84 (BB84) QKD protocol, where eavesdropping is detected if the measured quantum bit error rate (QBER) is equal to or higher than a threshold. The analysis clarifies the trade-off between the accuracy of eavesdropping detection and the economy of quantum resources in the BB84 protocol. Owing to the central limit theorem, the QBER measured by 300 quantum bits (qubits) is sufficient to guarantee lower than 0.009% of the FPR and FNR of eavesdropping detection. To deal with rapidly varying quantum channel conditions, this study further introduces grouped BB84 protocol and combinatory eavesdropping detection algorithms. A polarization basis is changeable for a group of qubits, and eavesdropping is judged by a combination of criteria between QBER and group-QBER in the proposed protocol and algorithms. In our extensive simulation study, the grouped BB84 protocol with 300 qubits comparison guarantees at least 99.92% accuracy in eavesdropping detection under rapidly varying quantum channel conditions. [ABSTRACT FROM AUTHOR]

Wireless virtualization has emerged as one of the efficient ways to overcome numerous wireless networking challenges such as resource sharing by allowing multiple concurrent virtual wireless networks having run on shared physical wireless infrastructure. Spectrum sharing in wireless networks focuses on increasing the usage efficiency of radio frequency (RF) spectrum with its increasing demand. To reduce uncertainties that accompany the spectrum sharing process caused by RF sensing, database-based spectrum sharing allows opportunistic spectrum access through occupancy information query of a spectrum database. In this article, we propose a 2-level hierarchical spectrum allocation framework for multiple service-based virtual networks (VN) by leasing wireless resources from Wireless Infrastructure Providers (WIPs). The framework leverages the software defined network (SDN)-enabled virtualization where network parameters are configured based on the operating environment on the fly. This article focuses on the competition between multiple VNs to get best RF channels to serve their users while eliminating the need for one-to-one contracts between VNs and WIPs. We develop a mathematical model for the spectrum allocation scheme which allows heterogeneous network configurations with better economic benefit. Using network segments formed based on available sharable resources, the allocation scheme is localized which makes the scalability of this scheme possible. A trust-based security mechanism is also proposed for early detection and prevention of security attacks to maintain the availability attribute of the resource allocation process. Simulation results show that spectrum sharing is efficient, and assures service availability to perceived legitimate VN users. [ABSTRACT FROM AUTHOR]

The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this article, we consider multiuser and multi-RIS-empowered wireless systems and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on deep reinforcement learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth-generation (6G) era are presented along with some key open research challenges. Different from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multiarmed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional deep $Q$ network (DQN) algorithm, but with lower implementation complexity. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are 2-D metasurfaces, which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth-generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this article, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. First, the concept, principle, and potential applications of RIS are given. An overview of RIS-based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS-based channel characteristics are studied, including reflection and transmission, the Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field, near field, and so on. RIS-based channel modeling works are investigated, including large-scale path loss models and small-scale multipath fading models. Finally, future research directions related to RIS-assisted channels are also discussed. [ABSTRACT FROM AUTHOR]

This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies $(\epsilon,\delta)$ -differential privacy and the privacy budget per worker scales as $\mathcal {O}(\frac{1}{\sqrt{N}})$ , in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of $\mathcal {O}(\sqrt{\frac{1}{TN}})$ as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT), a core enabler of Industry 4.0, is evolving rapidly to tackle the challenges imposed by explosive real-time manufacturing data in the context of Internet and telecommunication industry. 5G technology is the key to addressing such challenges. This is done by bypassing upper authentication protocols and supporting small data transmission during initial access, which, however, causes serious security breaches in IIoT device authentication. To solve this, in this article propose a secure cross-layer authentication framework based on quantum walk on circles. The system performs random hash coding on multidomain physical-layer resources to encode and decode device identifiers securely, while using a quantum walk based privacy-preserving protocol to maintain code privacy at arbitrary high level, being controlled by the number of occupied physical resources. The upper bound of decoding errors is derived and a nonconvex integer programming problem of minimizing the bound is formulated to characterize the security performance. The space of one-time keys for encryption is also derived that show how high privacy and scalability advantage is maintained against classical and quantum computers. Finally, we derive novel expressions of failure probability of this new authentication system and numerically show that our scheme can bring ultrahigh level of security and privacy protection with low latency despite attack. [ABSTRACT FROM AUTHOR]

We propose novel reconfigurable-intelligent-surface (RIS)-based energy harvesting (EH) systems with linear EH (L-EH) and non-linear EH (NL-EH) models where channels are exposed to Nakagami- $m$ fading. We also compare two RIS-based EH systems in terms of RIS locations, namely RIS-EH and RIS-information processing (RIS-IP). In the RIS-EH, RIS increases the amount of harvested energy, while in the RIS-IP, RIS is utilized to improve the information processing link. Closed-formed expressions of throughput, outage probability, and average harvested power are derived and confirmed via simulation. The L-EH model overestimates system performance, while the NL-EH model provides a realistic interpretation of EH system design. [ABSTRACT FROM AUTHOR]

We study communication in the presence of a jamming adversary where quadratic power constraints are imposed on the transmitter and the jammer. The jamming signal is allowed to be a function of the codebook, and a noncausal but noisy observation of the transmitted codeword. For a certain range of the noise-to-signal ratios (NSRs) of the transmitter and the jammer, we are able to characterize the capacity of this channel under deterministic encoding or stochastic encoding, i.e., with no common randomness between the encoder/decoder pair. For the remaining NSR regimes, we determine the capacity under the assumption of a small amount of common randomness (at most $2\log (n)$ bits in one sub-regime, and at most $\Omega ({n})$ bits in the other sub-regime) available to the encoder-decoder pair. Our proof techniques involve a novel myopic list-decoding result for achievability, and a Plotkin-type push attack for the converse in a subregion of the NSRs, both of which may be of independent interest. We also give bounds on the strong secrecy capacity of this channel assuming that the jammer is simultaneously eavesdropping. [ABSTRACT FROM AUTHOR]

With the development of 5G, it is unsurprising that most of the smart devices in the Internet of Things (IoT) will be wirelessly connected with each other in the near future. This kind of lightweight, scalable and green network architecture will be well-received. In a wide variety of IoT application scenarios, sensor nodes deployed in a local space, such as a multistory building, automatically form a distributed 3D wireless IoT and it can be employed to collect and analyze environmental information. Source-location privacy protection is of great importance in these networks and however, most existing schemes focus on only planar distributed networks which are not suitable for the 3D networks. In this paper, we consider a novel trace-back attack for 3D wireless IoT and then design a source-location privacy protection scheme, named DMR-3D, to defend this kind of novel attacks. In DMR-3D, the source node first selects a set of virtual locations to indirectly choose a set of agent nodes based on the cold start sphere structure and the ellipsoid communication pipeline. Then, a sophisticated mechanism is designed based on both the connected graph and Multiple Delaunay Triangulation (MDT) structure of the network to deliver packets from the source node to the destination node via these agent nodes in a relay manner. Analysis and simulation results illustrate that the proposed scheme can effectively protect source-location privacy with a moderate increment of path stretch, time delay and data transmission amount. [ABSTRACT FROM AUTHOR]

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure computation offloading under the eavesdropping-attack, in which a malicious node overhears the edge-computing user's (EU’s) offloading transmission to the edge-computing server (ES), and NOMA is used for the EU's offloading and meanwhile for providing artificial jamming to the eavesdropper. Since multi-user simultaneous transmission over a same frequency channel can be enabled by NOMA, a wireless user (WU) can form a NOMA pair with the EU to provide cooperative jamming to the eavesdropper while also gaining an opportunity of sending its data. Focusing on the EU-WU pair with the fixed WU's energy-provisioning, we exploit the physical layer security to quantify the EU's offloading throughput with the help of WU's jamming. We then study the joint optimization of the EU's computation offloading and the EU-WU's NOMA transmission for minimizing the EU's total energy consumption subject to its latency-requirement in completing the computation-task. By utilizing the feature of analytical solution of the WU's transmission, we then investigate the WU's optimal energy-provisioning for the EU-WU pair, such that both the EU and WU can benefit from the cooperative jamming in a fairness manner. Specifically, we formulate the EU-WU's cooperation as a Nash bargaining game. By identifying the monotonic feature of Nash bargaining problem, we propose a polyblock approximation based algorithm for determining the WU's optimal energy-provisioning to achieve the win-win solution for the paired EU and WU. Finally, we investigate the scenario of multiple EUs and WUs, and aim at finding the stable pairing between the EUs and WUs, such that no individual EU (or WU) would like to change its partner. An efficient algorithm, which is based on the Gale-Shapley theory while exploiting the quantitative feature of EUs’ and WUs’ net-rewards, is proposed to achieve the stable EU-WU pairings. Numerical results are provided to validate our proposed algorithms and demonstrate the advantage of our proposed NOMA assisted computation offloading via cooperative jamming. [ABSTRACT FROM AUTHOR]

Covert communication can hide the legitimate transmission from unauthorized eavesdropping. Benefiting from the deployment flexibility, unmanned aerial vehicles (UAVs) can be utilized to enhance communication confidentiality. In this correspondence, we consider a covert communication network with the aid of a full-duplex UAV relay, which is employed to help the transmission and confuse the warden. The warden adopts a radiometer to detect the covert transmission. We first find the optimal detection threshold and calculate the minimum detection error probability. Furthermore, a closed-form expression of outage probability via UAV relaying is derived over Rician fading. Then, a power optimization problem is formulated to maximize the effective convert throughput with covertness constraint. Numerical results illustrate that the cooperative jamming can disrupt the warden, and the optimal power tradeoff can guarantee the covert transmission effectively. [ABSTRACT FROM AUTHOR]

Covert communication has emerged as a revolutionary wireless technique to hide confidential information - either naturally or via artificial noise - from being detected. Random transmit power, time-variant channel state information, and location mobility can be leveraged to confuse the detection at the warden. However, it becomes challenging when the distance is long or the direct link is blocked. In this article, we explore an unmanned aerial vehicle (UAV) as the relay to assist covert wireless networks to achieve better quality of service. First, several typical application scenarios are demonstrated and briefly discussed. Then, two scenarios of UAVs serving as a relay in covert wireless networks are narrated. The first case investigates a multi-antenna transmitter aided by a drone relay in the long-range covert wireless network against the detection of a UAV warden. The second case studies the covert wireless network assisted by a UAV relay to overcome the channel blockage problem. Simulation results are provided to evaluate the effectiveness and superiority of the proposed schemes. Finally, some future challenges are discussed. [ABSTRACT FROM AUTHOR]

Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security. [ABSTRACT FROM AUTHOR]

The progress in biomedical sensors, Internet of Things technologies, big data, cloud computing, and artificial intelligence is leading the development of e-health medical systems, offering a range of new and innovative services. One such service is remote patient monitoring, where medical professionals are able to collect and examine a patient's medical data remotely. Of course, in these systems, security and privacy are of utmost importance and we need to verify the identities of system users before granting them access to sensitive patient-related data. To this end, several authentication protocols have been recently designed specifically for e-health systems. We survey several of these protocols and report on flaws and shortcomings we discovered. Moreover, we propose an authentication protocol that enables a medical professional and the network of sensors used by a patient to authenticate each other and share a cryptographic key to be used for security in a communication session. The protocol also enables the dynamic assignment of patients to doctors in order to control access to patients' data. We perform a security analysis of the protocol both formally, using the ProVerif protocol analysis tool, and informally, demonstrating its security features. We show that our protocol achieves mutual authentication, secret key establishment, forward secrecy, and anonymity. In terms of performance, the protocol is computationally lightweight, as it relies on symmetric key cryptography. This is demonstrated by comparing the computational cost of our protocol (in terms of execution time) with that of other similar protocols. [ABSTRACT FROM AUTHOR]

This paper investigates the power allocation problem for secure multiple-input single-output transmission with the injection of artificial noise (AN), in the presence of an unknown eavesdropper (Eve). Two power allocation schemes, the optimal adaptive power allocation (OAPA) and suboptimal fixed power allocation (SFPA) schemes, are proposed to enhance the physical layer security of the considered system. Since the noise power at Eve is unknown, both power allocation schemes are designed for the worst-case scenario in which the noise power at Eve is assumed to be zero, aiming to minimize the secrecy outage probability (SOP). To characterize the performance of the proposed power allocation schemes, approximate closed-form expressions for average SOP under a preset noise power level are derived by applying Gauss-Chebyshev quadrature. We also address the worst-case secrecy outage performance for the proposed OAPA and SFPA schemes. Our analytical and numerical results show that, compared with the exhaustive search method that requires Eve’s prior information, the proposed OAPA scheme exhibits comparable secrecy outage performance without Eve’s prior information. Additionally, the SFPA scheme, also without Eve’s prior information, is capable of achieving almost the same worst-case SOP as the OAPA scheme, with a much lower implementation complexity. [ABSTRACT FROM AUTHOR]

The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission–critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high–speed and low–latency connections for a large number of resource–constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave–enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non–ideal manufacturing of antenna arrays used in mmWave–enabled devices. In our approach, when legitimate mmWave–enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G–NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource—constrained IoT nodes. In our experiments, we reached 98.6% accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We investigate covert wireless communication in a multi-antenna relay network, where the relay transmits its own covert message to the destination when assisting the source’s information delivery, and the source acts as a warden to detect this covert transmission. Based on whether the channel state information of the relay-destination link is available at the source or not, we propose two relay beamforming schemes, namely random beamforming and maximum-ratio transmission (MRT) beamforming schemes, to guarantee the reception reliability at the destination while deliberately introducing uncertainty to the source to degrade its detection. Under the worst-case covert communication scenario where the source is capable of optimizing its detection threshold, analytical expressions for the minimum detection error probability achieved by each of the proposed schemes are derived to evaluate the detection limits of the source. By utilizing the above analytical results as the covertness constraint, an optimization problem of transmit power allocation for each scheme is formulated and solved to maximize the covert rate. The impact of imperfect channel state information on the covert communication performance is also examined. Simulation results are performed to confirm the accuracy of the derived analytical results and quantify the communication covertness enhancement of the proposed schemes. Our results also show that the MRT beamforming scheme offers a higher covert rate than that of the random beamforming scheme, especially when the covertness constraint becomes loose and/or the number of antennas at the relay increases. [ABSTRACT FROM AUTHOR]