Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.
Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.
Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.
In this paper, we present a real-life, cost-effective implementation of physical layer security (PLS) using friendly jamming (FJ) and the IEEE 802.11 technology. Our approach is based on a recent development in software-defined networking (SDN) called spectrum programming, where a network controller can execute an intelligent access point (AP) selection algorithm to connect the user station to the AP that provides the most secrecy while exploiting idle APs as jammers. Considering a system with two APs, our first contribution is a theoretical optimization of the power of FJ based on system power parameters, as well as distances between the two APs and the user station and the eavesdropper station. Our second contribution is demonstrating not only that PLS can be implemented with commercial-off-the-shelf Wi-Fi devices, but also that our theoretical network-centric approach allows for a significant increase of secrecy capacity and secrecy coverage by applying FJ. Our experiments show that the theoretical optimization of the transmit power of the jamming AP is valid in practice, effectively maximizing the throughput gap between the user and the eavesdropper. To our best knowledge, this is the first work linking information-theoretic optimization of FJ in PLS to a real-world implementation, which is compatible with Wi-Fi standards and devices.
In recent years, physical layer security (PLS) has emerged as a promising concept to complement cryptography solutions. Many PLS schemes require perfect knowledge of the channel state information (CSI) at the transmitter. However, in practical cases, CSI is often imperfect due to channel estimation errors, noisy feedback channels and outdated CSI. In this paper, we study the impact of imperfect CSI on an adaptive PLS scheme that combines diversity with interleaving to provide security. Particularly, we derive the secrecy capacity expressions for the legitimate receiver and the eavesdropper's channels under imperfect CSI conditions. Numerical and theoretical simulations for secrecy capacity and bit error rate (BER) are carried out for the frequency-selective Rayleigh fading wiretap channel model. The results reveal the negative impact of imperfect CSI on the secrecy and BER performance of the single input single output (SISO) orthogonal frequency division multiplexing (OFDM) system. The analysis is done under both frequency division duplex (FDD) and time division duplex (TDD) modes.
Inspired by recent results on information-theoretic security, we consider the transmission of confidential messages over wireless networks, in which the legitimate communication partners are aided by friendly jammers. We characterize the security level of a confined region in a quasi-static fading environment by computing the probability of secrecy outage in connection with two new measures of physical-layer security: the jamming coverage and the jamming efficiency. Our analysis for various jamming strategies based on different levels of channel state information provides insight into the design of optimal jamming configurations and shows that a single jammer is not sufficient to maximize both figures of merit simultaneously. Moreover, a single jammer requires full channel state information to provide security gains in the vicinity of the legitimate receiver.
Covert communication and physical layer security (PLS) have emerged as two essential pillars for safeguarding wireless networks against increasingly sophisticated eavesdropping and surveillance threats. This paper investigates a downlink hybrid power–frequency multiple access (HPMA) system aided by a friendly jammer, where a single source communicates with two legitimate users in the presence of a passive eavesdropper. By jointly exploiting the power and frequency domains, the proposed HPMA scheme achieves clear advantages in terms of covertness and secrecy performance compared to conventional orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. For performance analysis, we derive closed-form and approximated expressions for key metrics, including detection error probability (DEP), secrecy outage probability (SOP), effective secrecy throughput (EST), and average secrecy capacity (ASC), which reveal important trade-offs among resource allocation, jammer power, and secrecy reliability. To further reduce the computational burden of conventional analysis, a regression-based deep neural network (DNN) framework is developed to efficiently predict the secrecy performance of the considered system. Extensive Monte Carlo simulations validate the analytical results and confirm the superiority of the proposed HPMA scheme with friendly jamming. These findings provide useful insights for the design of future secure and covert wireless systems with low-complexity and intelligent physical-layer security requirements.
As the number of connected devices and the importance of mobile communications continue to increase, a greater emphasis must be placed on security. Due to the broadcast nature of wireless communications, wireless networks are very exposed to eavesdropping. While this can be addressed above the physical layers using encryption, this still allows the attacker to receive the message and future work may allow decryption. Physical layer security is an approach to security which exploits the wireless channel to prevent the attacker from decoding the message. This thesis examines the use of friendly jamming, in which some nodes in a network broadcast white noise in order to degrade the channel between the legitimate transmitter and the eavesdropper. We address two problems related to the use of friendly jamming to improve physical layer security. The first problem is routing a signal through a network while using the remaining nodes as jammers to secure the signal. This is solved as two convex problems of allocating power to the jammers and routing the signal using those jammers to secure the transmission. This is shown to be a feasible method to increase security in a network. The second problem is estimating the self-interference channel (SIC) without using a calibration period for full-duplex jamming receivers. As the transmitter cannot transmit while the receiver estimates its SIC using a half duplex pilot signal, eliminating the calibration period can represent a significant capacity gain. Estimating the channel while receiving the desired signal causes it to act as an additional noise source, but this is shown to be overcome through the use of long estimation times. Our proposed scheme is able to increase the secrecy capacity of the system over that of calibration based estimation.