Layer Security In Wireless Networks Research Articles

A novel sparse-based approach for joint radio frequency fingerprint and channel estimation

Radio Frequency Fingerprint (RFF) plays a pivotal role in specific emitter identification (SEI) and enhancing the physical layer security of wireless networks. However, past research on RFF extraction has encountered significant challenges, including algorithm instability in the presence of multi-path channels, disparities between actual RFF and proposed models, and the need to compensate for receiver RFF. In this study, we introduce an innovative algorithm designed to simultaneously estimate the transmitter's RFF and the non-line-of-sight (NLOS) channel state in the presence of receivers’ antenna effect. Moreover, we introduce the transmitter antenna feeding current as a modified RFF, accounting for transimtter components such as power amplifier (PA) nonlinearity, in-phase quadrature (IQ) modulator imbalance. Our approach commences with an exploration of wave propagation theory fundamentals, followed by a transformation of the inverse scattering equations into linear equations for joint channel and RFF estimation. Due to the non-convex nature of the derived equation, we employ the sparse lift method to render the problem convex. Leveraging the sparsity of channel coefficients, we devise a compressive sensing linear solution, denoted as the sparse-based approach for joint (SBJ) RFF and channel estimator. Additionally, we propose a calibration method to mitigate the effect of the receiver antennas on the estimated RFF of the transmitter. Through simulation, we demonstrate a 10 dB enhancement in RFF estimation accuracy compared to existing methods. Furthermore, our results indicate that the SBJ algorithm remains robust when faced with the combined effect of transmitter components, whereas previous models exhibit diminished precision as additional component effects are considered.

Artificial Noise and RIS-Aided Physical Layer Security: Optimal RIS Partitioning and Power Control

The synergism of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) shows significant promise in improving physical layer security in wireless networks. Accordingly, this letter proposes the virtual partitioning of RIS elements into two parts such that the phase shifts of the different partitions are configured to improve the intended signal at a legitimate user and enhance the impact of AN on an illegitimate user, respectively. To this aim, two problems are defined to jointly optimize the partitioning ratio, and signal/noise transmit power levels for two main objectives. First, we maximize secrecy capacity by satisfying users’ quality of service (QoS). Second, we optimize transmit power to establish a secure link by satisfying the QoS of the legitimate user. We provide closed-form solutions subject to the rate constraints on both legitimate and illegitimate users. Simulation results validate the closed-from solutions and show that the proposed RIS-partitioning method dramatically improves SC compared to benchmark methods.

Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches.

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.

Secrecy Energy Efficiency Optimization for Reconfigurable Intelligent Surface-Aided Multiuser MISO Systems

Currently, the reconfigurable intelligent surface (RIS) has been applied to improve the physical layer security in wireless networks. In this paper, we focus on the secure transmission in RIS-aided multiple-input single-output (MISO) systems. Specifically, by assuming that only imperfect channel state information (CSI) of the eavesdropper can be obtained, we investigated the robust secrecy energy efficiency (SEE) optimization via jointly designing the active beamforming (BF), artificial noise (AN) at Alice, and the passive phase shifter at the RIS. The formulated problem is hard to handle due to the complicated secrecy rate expression as well as the infinite constraints introduced by the CSI uncertainties. By utilizing the Taylor expansion, we transformed the fractional programming into a convex problem, while all the constraints are approximated via the successive convex approximation and constrained concave-convex procedure. Then, by using the extended S-Lemma, we transform the infinite constraints into linear matrix inequality, which is convex. Finally, an alternate optimization (AO) algorithm was proposed to solve the reformulated problem. Simulation results demonstrated the performance of the proposed design.

Assessment of Features and Classifiers for Bluetooth RF Fingerprinting

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.

Physical Layer Security in Wireless Networks With Ginibre Point Processes

In this paper, we investigate wireless networks consisting of a legitimate transmitter (Alice), a legitimate receiver (Bob), eavesdroppers (Eves), and friendly jammers. Two network scenarios are considered depending on whether Alice and the jammers have the ability to detect the existence of Eves in their vicinity. If they do not have the ability, as a means to enhance the secrecy, Alice transmits artificial noise and each jammer selectively radiates a jamming signal based on the channel gain between the jammer and Bob. On the other hand, when they have the ability, Alice sends a confidential message to Bob if no Eve is detected within its guard zone, and the jammers transmit jamming signals when there exists at least one Eve in their vicinity. We model the spatial distributions of Eves and jammers as $\beta $ -Ginibre point processes, which can characterize repulsion among the nodes and include the Poisson point process (PPP) as a special case. Then, we analyze both the probability that Bob successfully decodes the confidential message and the probability that the message is secure against eavesdropping. Also, we show that our analysis is a generalization of previous works on the networks with PPPs by recovering them from our analytical results.

Tradeoff Between Delay and Physical Layer Security in Wireless Networks

Exchange of crucial and confidential information leads to the unprecedented attention on the security problem in wireless networks. Even though the security has been studied in a number of works, the joint optimization of the physical layer security and the end-to-end delay management, which requires a meticulous cross-layer design, has seldom been evaluated. In this paper, by combining the tools from stochastic geometry and queueing theory, we analyze the tradeoff between the delay and the security performance in large wireless networks. We further propose a simple transmission mechanism which splits a message into two packets and evaluate its effect on the mean delay and the secrecy outage probability. Our numerical results reveal that the security performance is better for larger path loss exponent when the density of legitimate nodes is large, and it is reverse when the density is small. Moreover, it is observed that by introducing the simple mechanism of message split, the security performance is greatly improved in the backlogged scenario and slightly improved in the dynamic scenario when the density of legitimate transmitters is large. In summary, this paper provides an understanding and a rule-of-thumb for the practical design of wireless networks where both the delay and the security are key concerns.

Fast Power Allocation for Secure Communication With Full-Duplex Radio

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy.

An Analysis Modeling Architecture for Supporting Physical Layer Security of Wireless Networks under Hardware Impairments

In this paper, we study physical layer security (PLS) of wireless networks in presence of one eavesdropper. Assume that the channel state information (CSI) of the eavesdropper cannot be obtained at the transmitter, such as CSI can be then estimated through the CSI of a torch node. In addition, a practical issue of wireless nodes, i.e., hardware impairment (HI), is considered at the source node of the network. System performances in terms of secrecy outage probability, probability of non-zero secrecy capacity and ergodic secrecy capacity are then investigated over Rayleigh fading channels. In particular, the closed-form expressions of the secrecy outage probability and probability of non-zero secrecy capacity are provided. Analytical results are then validated by Monte-Carlo simulation. Numerical results reveal the effects of the HI and the advantages of the use of the torch node in PLS of wireless communications.

Physical layer security in wireless networks: a tutorial

Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics.