Physical layer security (PLS) has been widely employed in the Internet-of-Things (IoT). Since most IoT devices are energy-constrained, the power efficiency of PLS schemes attracts increasing attention. Distinguished with existing power optimization-based PLS schemes, we propose a novel PLS scheme by optimizing the transmit phase to guarantee the security in an energy efficient way. On one hand, we add an initial phase shift at the legitimate transmitter to enlarge the difference between the received signal at the legitimate receiver and the eavesdropper. On the other hand, we employ a reconfigurable intelligent surface (RIS) to enhance the interference at the eavesdropper. Aiming at increasing the bit error rate (BER) at the eavesdropper with low energy cost, the phase optimization problem is solved by using a deep deterministic policy gradient (DDPG) algorithm, which is based on a deep reinforcement learning framework. Simulation results demonstrate that our proposed phase optimization scheme can effectively improve the security performance and reduce the power consumption at the transmitter.
Physical layer security (PLS) has been widely employed in the Internet-of-Things (IoT). Since most IoT devices are energy-constrained, the power efficiency of PLS schemes attracts increasing attention. Distinguished with existing power optimization-based PLS schemes, we propose a novel PLS scheme by optimizing the transmit phase to guarantee the security in an energy efficient way. On one hand, we add an initial phase shift at the legitimate transmitter to enlarge the difference between the received signal at the legitimate receiver and the eavesdropper. On the other hand, we employ a reconfigurable intelligent surface (RIS) to enhance the interference at the eavesdropper. Aiming at increasing the bit error rate (BER) at the eavesdropper with low energy cost, the phase optimization problem is solved by using a deep deterministic policy gradient (DDPG) algorithm, which is based on a deep reinforcement learning framework. Simulation results demonstrate that our proposed phase optimization scheme can effectively improve the security performance and reduce the power consumption at the transmitter.
Securing communication between multiple users efficiently while there are too many potential eavesdroppers has become an important issue with the rise of the Internet of Things (IoTs). This paper extends on earlier research, moving from a single‐user and single‐eavesdropper scenario to a complex multiuser and multieavesdropper context, and incorporates an advanced physical layer security (PLS) technique for the first time. Using reconfigurable intelligent surfaces (RISs) enhances the strength and quality of signals for intended users, while those to the unintended users are suppressed. Real‐time control of the RIS phase shifts is enabled through a deep deterministic policy gradient (DDPG) algorithm and this control significantly changes the trade‐off between security and energy wastage. The simulation results demonstrate that the developed approach can scale up in densely populated urban centers, while increasing the bit error rate (BER) performance and the overall energy efficiency across different wireless mobile channels.
The escalating demand for secure wireless communication, driven by the proliferation of connected devices, has made information security a paramount concern. Traditional encryption methods struggle against eavesdropping and malicious jamming in open wireless channels, necessitating innovative solutions. This paper explores physical layer security, leveraging intrinsic wireless channel properties, enhanced by reconfigurable intelligent surface (RIS). We propose an RIS-assisted multiple-input single-output system to counteract eavesdroppers and jamming threats through an artificial noise (AN) cooperation scheme. AN is injected during legitimate beamforming to bolster transmission security. Jointly optimizing the base station’s precoding vector, AN vector, and RIS reflection coefficients under power, quality, and phase constraints, we formulate a non-convex secrecy rate (SR) maximization problem. Addressing its complexity, we model it as a Markov decision process and develop a deep deterministic policy gradient algorithm within a deep reinforcement learning framework. Simulation results demonstrate the proposed method’s superior performance, reduced complexity, and robustness against multiple eavesdroppers and high-power jamming, underscoring the critical roles of RIS and AN in securing wireless communications. It consistently outperforms traditional algorithms such as alternating optimization with semidefinite programming, offering up to 7% higher SR and achieving nearly 120% SR gains as RIS elements scale from N =4 to N =10.
Physical-layer key generation (PLKG) is an information-theoretic security technique aimed at addressing key distribution challenges among resource-constrained legitimate users in the Internet of Things (IoT). However, in quasi-static or blocked environments, key generation faces significant limitations owing to its low entropy. To overcome this challenge, we propose leveraging a simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS). In this study, we introduce a STAR-RIS-aided multiuser PLKG framework and formulate an optimization problem focused on minimizing the average bit disagreement ratio through optimal phase-shift matrix adjustments. Initially, we derive closed-form solutions for optimal phase shifts, simplifying the problem to depend solely on amplitude coefficients. We then employ a Newton-Raphson algorithm under the energy-splitting (ES) protocol and a traversal method under the mode-switching (MS) and time-switching (TS) protocols to solve the optimization problem. Simulation results confirm the efficacy of our proposed schemes by comparison with existing benchmark schemes.
Physical layer security has emerged as a promising approach to counter eavesdropping threats in the Internet of Things (IoT). With the advancement of technology, eavesdroppers (Eves) are increasingly sophisticated, exacerbating the difficulties in ensuring the security of uplink transmissions. In situations where transmitters are unable to deter Eves from approaching at close range, these intelligent Eves can attain substantial channel gains, thereby posing significant security threats. To address these challenges, we first propose a time-domain artificial noise (AN) scheme to eliminate interference at the legitimate receiver in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Then, we model the adversarial interaction between legitimate transceivers and intelligent Eves as a game-theoretic problem. To solve the dynamic game problem, we introduce a deep deterministic policy gradient (DDPG)-based framework, where an optimal beamforming fractional programming algorithm (OBFP) and a lower-complexity zero-forcing beamforming difference of convex algorithm (ZFDC) are designed to ascertain the most effective strategy for legitimate transceivers. Simulation results validate the effectiveness of our proposed algorithms and highlight the critical role of time-domain AN in combating intelligent eavesdropping.
The Internet of Things (IoT) is one of the most promising technological revolutions. However, the IoT is inherently vulnerable to attacks. In this paper, we propose to enhance the physical layer security of the IoT by using a technique called information passing and interference addition. In the physical layer security analysis, the achieved secrecy rate has an inverse relationship with the intruder's distance to the legitimate transmitter. Our work is, first, to increase the achievable secrecy rate by letting the legitimate transmitter find an alternative route to the fusion center that could achieve a higher secrecy rate than the direct route. Hence, the legitimate transmitter must not disclose the actual information to the fusion center if there is an eavesdropper located between them, and rather must pass its information to another node via a directional antenna to concentrate the power towards the intended receiving node. Passing the information to the nearest node must happen under the condition that there is no intruder in between. Second, we use simultaneous multi-stream beamforming transmission to strengthen the legitimate receiver's signal-to-noise ratios and at the same time poison the eavesdropper channel with interference. Third, we disclose false information to the intruder. We created the system design and investigated the secrecy analysis of that system. Also, we discussed the use of directional antennas on the legitimate nodes. The result showed that using directional antennas would increase the probability of having at least one secure connection coming out of the node to the other legitimate nodes. Moreover, the results showed that using the simultaneous multi-stream beamforming improved the legitimate channel's achievable secrecy rate and that it has superiority over the directional antenna effect.
Establishing robust cybersecurity for Internet of Things (IoT) ecosystems poses significant challenges for system operators due to IoT resource constraints, trade-offs between security and performance, diversity of applications, and their security requirements, usability, and scalability. This article introduces a physical-layer security (PLS) approach that enables IoT devices to maintain specified levels of information confidentiality against wireless channel eavesdropping threats. This work proposes applying PLS active defense mechanisms utilizing spectrum-sharing schemes combined with fair scheduling and power management algorithms to mitigate the risk of eavesdropping attacks on resource-constrained IoT environments. Specifically, an IoT device communicating over an insecure wireless channel will utilize intentional noise signals transmitted alongside the actual IoT information signal. The intentional noise signal will appear to an eavesdropper (EVE) as additional noise, reducing the EVE’s signal-to-interference-plus-noise ratio (SINR) and increasing the EVE’s outage probability, thereby restricting their capacity to decode the transmitted IoT information, resulting in better protection for the confidentiality of the IoT device’s transmission. The proposed communication strategy serves as a complementary solution to existing security methods. Analytical and numerical analyses presented in this article validate the effectiveness of the proposed strategy, demonstrating that IoT devices can achieve the desired levels of confidentiality.