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.
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.
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.
6G communication systems, particularly massive Internet of Things (IoT), face critical security challenges in safeguarding transmissions against eavesdropping attacks. These challenges are exacerbated by the presence of intelligent eavesdroppers capable of exploiting impairments in wiretap channels. Traditional physical layer security (PLS) techniques, such as friendly jamming (FJ), typically rely on the differentiability and accurate availability of channel state information (CSI) to optimize performance. However, in real-world scenarios, non-differentiable channels (NDCs) resulting from hardware imperfections, mobility, and complex multi-path fading, pose significant obstacles to conventional gradient-based optimization methods. In this paper, we propose a novel deep learning-based FJ approach tailored specifically for NDC environments, where gradient-based techniques prove ineffective. Leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm, our framework dynamically generates jamming signals to optimize secrecy rates while simultaneously minimizing the block error rate (BLER) at the legitimate receiver. Through extensive evaluation of realistic channel models, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, our proposed approach demonstrates superior security enhancements and robust performance against eavesdropping threats. The results highlight its effectiveness in securing communications under the challenging and dynamic conditions inherent to NDC environments.
In this paper, we propose a best node and friendly jammer (bN-fJ) scheme for secure short-packet multi-hop communications in Internet-of-Things (IoT) networks. Under imperfect channel state information (CSI) conditions, the best IoT node is chosen for data transmission and a friendly jammer is chosen later to confuse the received signals at a multi-antenna eavesdropper. Approximate and asymptotic closed-form expressions for the secrecy throughput of the bN-fJ scheme are obtained, offering valuable insights into the system designs. Numerical results show that the proposed bN-fJ scheme achieves much better performance than benchmarking schemes, especially with 1 more bit/channel use and 9.4 dB enhancement in communication reliability measure. Moreover, under imperfect CSI and large antennas at the eavesdropper, the secrecy throughput and communication reliability of the system can be improved by employing the proposed node selection, e.g., a 12.5 dB improvement at 10 antennas at Eve and the imperfect CSI of 0.8. Finally, the secrecy throughput is presented as a concave curve for the number of hops and blocklengths, which enables us to identify the optimal hops and blocklength for secure multi-hop short-packet transmissions.
Traditional approaches for providing confidentiality of wirelessly transmitted signals are often based on cryptographic techniques. While these techniques ensure that messages are encrypted and secured against eavesdropping, they are not sufficient to protect important transmission attributes at the physical (PHY) layer. Specifically, several elds in the headers of PHY and medium access control (MAC) frames are typically sent unencrypted to maintain proper protocol functionality (e.g., sender/receiver identification). As a result, they leak side-channel information (SCI), including payload size, frequency and phase oset, modulation and coding scheme, identities of communication nodes, frame type, transmission rate, etc. These SCI can be exploited by an adversary to launch various passive (eavesdropping) and active (jamming) attacks. To complement cryptographic techniques used at upper layers and prevent the leakage of SCI at the PHY/MAC layers, PHY-layer security techniques have been introduced. These techniques exploit the characteristics of the wireless channel along with multiple-input multiple-output (MIMO) technologies, in which multiple transmitting and receiving antennas are utilized to prevent information leakage to eavesdroppers and/or avoid jamming attacks. In particular, if the signal-to-interference-plus-noise ratio (SINR) at an adversary is lower than the one at a legitimate receiver, secure communication can be achieved through the so-called wiretap coding. MIMO systems enable the transmitter and/or the receiver to generate artificial noise, called friendly jamming (FJ), so as to reduce the SINR at the adversary without impacting the SINR at the legitimate receiver. In this dissertation, we focus on exploring the security threats to next-generation wireless systems. We develop MIMO-based PHY-layer security methods to achieve reliable and secret communications in these systems. First, we consider a broadcast channel, in which a multi-antenna transmitter (Alice) sends K confidential information signals to K legitimate receivers (Bobs) in the presence of L eavesdropping devices (Eves). Alice uses MIMO precoding to generate the information signals along with her own Tx-based friendly jamming (TxFJ). Interference at each Bob is removed via a technique called MIMO zero-forcing. This TxFJ, however, leaves an eavesdropping \vulnerability region" in the proximity of each Bob, which can be exploited by a nearby Eve. Specically, because of the high correlation in the channel state information (CSI) at a Bob and nearby Eves, the TxFJ becomes less effective in preventing eavesdropping at these Eves. We address this problem by augmenting TxFJ with receiver-based friendly jamming (RxFJ), generated by each Bob. Specifically, we allow each Bob to use self-interference suppression (SIS) techniques to transmit a friendly jamming signal while simultaneously receiving an information signal over the same channel. These SIS techniques exploit circulators to attenuate the self-interference power of RxFJ at Bobs and utilize analog/digital cancellation methods to clean out any remaining residual self-interference. Considering the resulting broadcast channel, we rst study a two-user scenario and develop a secrecy encoding scheme for constructing the signals intended to two Bobs. We characterize the corresponding achievable secrecy sum-rate and formulate an optimization problem to maximize this metric. We then extend our approach to K-user scenarios and study a power minimization problem. Particularly, in our analysis we minimize the powers allocated to information and friendly jamming signals (both TxFJ and RxFJ) while guaranteeing individual secrecy rates for various Bobs. The problem is solved for the cases when the eavesdropper's CSI (ECSI) is known or unknown (but with a known distribution). Simulations show the effectiveness of the proposed solutions. Furthermore, we discuss how to schedule transmissions when the rate requirements need to be satised on average rather than instantaneously. In low and high power regime, a scheduling algorithm that serves only the strongest receivers is shown to outperform the one that schedules all receivers. Next, we consider a single-cell massive MIMO system in which a base station (BS) with a large number of antennas transmits simultaneously to several single antenna users. The BS acquires the CSI for various receivers using uplink pilot transmissions. We demonstrate the vulnerability of the CSI estimation process to a pilot-contamination (PC) attack that aims at minimizing the sum-rate of downlink transmissions by contaminating uplink pilots. We rst study PC attacks for two downlink power allocation strategies under the assumption that the attacker knows the locations of the BS and its users. Later on, we relax this assumption and consider the case when such knowledge is probabilistic. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the \achievable individual secrecy rates" under PC attacks, and provide an upper-bound on these rates. We also study this scenario without a priori knowledge of user locations at the attacker by introducing chance constraints. Another attack model is considered, where the attacker generates jamming signals in both the CSI estimation and data transmission phases by exploiting inband full-duplex techniques. Our results indicate that such attacks can degrade the throughput of a massive MIMO system by more than 50%, and reduce fairness among users significantly.
We investigate the opposite of artificial noise (AN)-assisted communication in multiple-input–multiple-output (MIMO) wiretap channels for the multiuser case by taking the side of the eavesdropper. We first define a framework for an AN-assisted multiuser multiple-input–multiple-output (MU-MIMO) system, for which eavesdropping methods are proposed with and without knowledge of legitimate users’ channel state information (CSI). The proposed method without CSI is based on a modified joint approximate diagonalization of eigen-matrices algorithm, which eliminates permutation indetermination and phase ambiguity, as well as the minimum description length algorithm, which blindly estimates the number of secret data sources. Simulation results show that both proposed methods can intercept information effectively. In addition, the proposed method without legitimate users’ CSI performs well in terms of robustness and computational complexity.
In this paper, we study secure cooperative transmission for the multiple-input-single-output multiple-eavesdropper (MISOME) systems, where a source (Alice) aims to send confidential messages to a legitimate user (Bob) with the aid of an unmanned aerial vehicle (UAV) enabled friendly jammer (Charlie) in the presence of multiple eavesdroppers (Eves). To further guarantee the secrecy performance, artificial noise (AN) beamforming together with cooperative Jamming (CJ) has been utilized in this considered system with only location and statistical channel state information of the Eves. Specifically, taking imperfect channel state information (CSI) between Charlie and Bob into account, we derive the exact closed-form expression of the secrecy outage probability (SOP) and develop an efficient scheme to solve the secrecy rate maximization (SRM) problem which has been verified as a convex one under certain SOP. Moreover, we propose an optimal UAV placement strategy to further enhance secrecy performance. The numerical results are provided to validate that the system security would seriously degenerate with channel uncertainty. Besides, the proposed scheme is verified to outperform the terrestrial CJ scheme in improving the secrecy rate and secure energy efficiency (EE) performance under perfect CSI or tolerable channel estimation errors. Finally, the effectiveness of the proposed UAV placement strategy in enhancing transmission security and the applicability of the proposed scheme has also been demonstrated.