Search Results (49)

Maliciously deployed disco intelligent reflecting surfaces (DIRSs) introduce active channel aging (ACA) to achieve fully passive jamming without requiring channel state information or jamming power. To enhance this capability, we propose a distributed DIRS framework for downlink multi-user multiple-input single-output (MU-MISO) systems. By distributing multiple panels, this framework increases independent reflection paths and introduces inter-panel cascaded reflections, severely exacerbating precoder mismatch. We develop a comprehensive near- and far-field cascaded channel model, deriving closed-form expressions for the interference variance and a sum-rate lower bound in the large-antenna regime. Both pilot training (PT) phase-on and phase-off scenarios are investigated to evaluate the jamming impact under different operational states. Analytical and simulation results reveal that DIRS-induced interference scales with transmit power, imposing a strict rate ceiling. Specifically, at 10 dBm transmit power per LU, the proposed framework not only reduces the achievable sum-rate by over 57% relative to the interference-free scenario, but also improves the jamming impact by approximately 36% compared to the conventional single-panel DIRS, demonstrating superior and robust fully passive jamming capability. Full article

Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for UAV swarm networks. Specifically, we first model the problem as a decentralized partially observable Markov decision process (Dec-POMDP), aiming to achieve a long-term trade-off between data transmission success rate and energy consumption. Then we propose a multi-agent independent advantage actor–critic (IA2C)-based energy-harvesting-assisted anti-jamming communication solution, which enables each cluster head (CH) to learn its transmit channel, power, and energy harvesting time policy independently. By constructing a time-space-based extended Dec-POMDP, the spatiotemporal correlations among neighboring nodes are learned by allowing adjacent agents to share discounted local observations. Extensive simulations show that, compared with the benchmark schemes, the proposed scheme improves the average cumulative reward and average cumulative success rate by 17.26% and 10.37%, respectively, while achieving a higher transmission success rate with lower energy consumption under different numbers of available channels. Full article

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. This paper provides a comprehensive survey of vulnerabilities and countermeasures in modern optical networks, spanning the physical, control, and cross-layer dimensions. We analyze major architectures—including WDM, TDM, PON, EON, and IP-over-WDM—and examine how their structural properties shape their security posture. A threat taxonomy is presented covering physical-layer attacks such as fiber tapping, optical jamming, crosstalk exploitation, and signal injection; control-plane risks including spoofing, malicious signaling, and SDN manipulation; and broader cross-layer attack vectors. We review state-of-the-art defense mechanisms, including physical-layer security (PLS), spectrum randomization, chaotic optical coding, device-level authentication, survivability techniques, intelligent monitoring, and quantum-secure solutions such as QKD. By integrating insights from recent experimental and operational studies, the survey highlights emerging challenges and identifies open problems related to secure orchestration, multi-tenant environments, and quantum-era resilience. The objective is to guide researchers, engineers, and network operators toward robust and future-proof security strategies for next-generation optical infrastructures. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

Dynamic spectrum access enables efficient anti-jamming in cognitive radio systems. However, in a multi-user distributed decision scenario, differences in spectrum states make collaboration among users a major challenge, especially when the sensing devices are heterogeneous. In order to solve this issue, we propose a collaborative anti-jamming cognitive radio system architecture based on historical jamming knowledge. Devices exhibiting high sensing performance support those exhibiting low sensing performance. An online reinforcement learning algorithm is used to learn the jamming patterns in real time. Finally, a multi-user collaborative anti-jamming system is developed using a software-defined radio platform. The anti-jamming performance of the system is verified experimentally under both internal communication jamming and external malicious jamming scenarios, achieving a jamming probability below 0.1. Full article

Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved Adaptive Backoff Algorithm (I-ABA), this paper proposes an Attack-Aware Adaptive Backoff Indicator (AA-BI) mechanism to enhance the security and robustness of the two-step random access process in LEO SI. The mechanism constructs a composite threat intensity indicator that incorporates collision probability, Denial-of-Service (DoS) attack strength, and replay attack intensity. This quantified threat level is smoothly mapped to a dynamic backoff window to achieve adaptive backoff adjustment. Simulation results demonstrate that, with 200 pieces of user equipment (UE), the AA-BI mechanism significantly improves the access success rate (ASR) and jamming resistance rate (JRR) under various attack scenarios compared to the I-ABA and Binary Exponential Backoff (BEB) algorithms. Notably, under high-attack conditions, AA-BI improves ASR by up to 25.1% and 56.6% over I-ABA and BEB, respectively. Moreover, under high-load conditions with 800 users, AA-BI still maintains superior performance, achieving an ASR of 0.42 and a JRR of 0.68, thereby effectively ensuring the access performance and reliability of satellite Internet in malicious environments. Full article

Unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems have developed rapidly in the sixth generation (6G) era. However, factors such as the mobility of ground users and malicious jamming pose significant challenges to systems’ performance and reliability. Against this backdrop, this paper designs a multi-UAV-assisted ISAC system model under malicious jamming environments. Under the constraint of sensing accuracy, the total communication rate of the system is maximized through joint optimization of user association, UAV trajectory, and transmit power. The problem is then decomposed into three subproblems, which are solved using the improved auction algorithm (IAA), dream optimization algorithm (DOA), and rapidly-exploring random trees-based optimizer algorithm (RRTOA). The global optimal solution is approached through the alternating optimization-based predictive scheduling algorithm (AOPSA). Meanwhile, this paper also introduces a long short-term memory (LSTM) network to predict users’ dynamic positions, addressing the impact of user mobility and enhancing the system’s real-time performance. Simulation results show that compared with the baseline scheme, the proposed algorithm achieves a 188% improvement in communication rate, which verifies its effectiveness and superiority. Full article

Low-Earth-orbit (LEO) satellite networks face significant vulnerabilities to malicious jamming and co-channel interference, compounded by dynamic topologies, resource constraints, and complex electromagnetic environments. Traditional anti-jamming approaches lack adaptability, centralized intelligent methods incur high overhead, and distributed intelligent methods fail to achieve global optimization. To address these limitations, this paper proposed a value decomposition network (VDN)-based multi-agent deep reinforcement learning (DRL) anti-jamming spectrum access approach with a centralized training and distributed execution architecture. Following offline centralized ground-based training, the model was deployed distributedly on satellites for real-time spectrum-access decision-making. The simulation results demonstrate that the proposed method effectively balances training costs with anti-jamming performance. The method achieved near-optimal user satisfaction (approximately 97%) with minimal link overhead, confirming its effectiveness for resource-constrained LEO satellite networks. Full article

Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious actors. In fact, an attacker can easily inject a malicious signal (jamming) with the aim of disrupting ongoing communications. In this paper, we propose a new solution to detect jamming attacks before they significantly affect the quality of the communication link, thus allowing the detection of a jammer (geographically) far away from a receiver. We consider two scenarios as a function of the receiver’s ability to know in advance the impact of the jammer on the received signal. In the first scenario (jamming-aware), we leverage a classifier based on a Convolutional Neural Network, which has been trained on both jammed and non-jammed signals. In the second scenario (jamming-unaware), we consider a one-class classifier based on autoencoders, allowing us to address the challenge of jamming detection as a classical anomaly detection problem. Our proposed solution can detect jamming attacks on PLC networks with an accuracy greater than 99% even when the jammer is 68 m away from the receiver while requiring training only on traffic acquired during the regular operation of the target PLC network. Full article

As the low-altitude economy undergoes rapid growth, unmanned aerial vehicles (UAVs) have served as mobile sink nodes in wireless sensor networks (WSNs), significantly enhancing data collection efficiency. However, the open nature of wireless channels and spectrum scarcity pose severe challenges to data security, particularly when legitimate UAVs (UAV-L) receive confidential information from ground sensor nodes (SNs), which is vulnerable to interception by eavesdropping UAVs (UAV-E). In response to this challenge, this study presents a cooperative jamming (CJ) scheme for Reconfigurable Intelligent Surfaces (RIS)-assisted UAV-WSN to combat aerial malicious eavesdropping. The multi-dimensional optimization problem (MDOP) of system security under quality of service (QoS) constraints is addressed by collaboratively optimizing the transmit power (TP) of SNs, the flight trajectories (FT) of the UAV-L, the frame length (FL) of time slots, and the phase shift matrix (PSM) of the RIS. To address the challenge, we put forward a Cooperative Jamming Joint Optimization Algorithm (CJJOA) scheme. Specifically, we first apply the block coordinate descent (BCD) to decompose the original MDOP into several subproblems. Then, each subproblem is convexified by successive convex approximation (SCA). The numerical results demonstrate that the designed algorithm demonstrates extremely strong stability and reliability during the convergence process. At the same time, it shows remarkable advantages compared with traditional benchmark testing methods, effectively and practically enhancing security. Full article

Global navigation satellite systems (GNSSs), with their ubiquitous coverage, have become a cornerstone of modern position, navigation, and timing (PNT) services. While their spread spectrum communication offers inherent, albeit partial, resilience against interference, GNSSs remain a prime target for malicious actors seeking to disrupt or degrade precise location and time synchronization. Jamming mitigation has been an active research area for over three decades. Despite diverse research efforts, a key weakness in the literature is the absence of rigorous, methodologically sound testing of proposed mitigation techniques in a controlled laboratory environment. This work addresses this deficiency by exploring the challenges of evaluating GNSS receiver performance and response under interference and by proposing a more robust methodological framework for result interpretation. We present a custom simulation environment that enables repeated, controlled measurements of GNSS receiver behavior under various jamming attacks, revealing discrepancies between expected performance and real-world observations. Using three low-cost receivers as a case study, we demonstrate the inherent uncertainty in the results, the unpredictable behavior of the receivers’ embedded software, and appropriate statistical analysis practices. A key contribution of this work is a publicly available dataset of extensive GNSS receiver response measurements acquired under controlled interference conditions using an advanced signal generation and a comprehensive satellite constellation simulator. Full article

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches. Full article

The scope of this work is to propose a distributed RF sensing architecture that interconnects and utilizes a cyber security operations center (SOC) to support long-term RF threat monitoring, alerting, and further centralized processing. For the purpose of this work, RF threats refer mainly to RF jamming, since this can jeopardize multiple wireless systems, either directly as a Denial of Service (DoS) attack, or as a means to force a cellular or WiFi wireless client to connect to a malicious system. Furthermore, the possibility of the suggested architecture to monitor signals from malicious drones in short distances is also examined. The work proposes, develops, and examines the performance of RF sensing sensors that can monitor any frequency band within the range of 1 MHz to 8 GHz, through selective band pass RF filtering, and subsequently these sensors are connected to a remote SOC. The proposed sensors incorporate an automatic calibration and time-depended environment RF profiling algorithm and procedure for optimizing RF jamming detection in a dense RF spectrum, occupied by heterogeneous RF technologies, thus minimizing false-positive alerts. The overall architecture supports TCP/IP interconnections of multiple RF jamming detection sensors through an efficient MQTT protocol, allowing the collaborative operation of sensors that are distributed in different areas of interest, depending on the scenario of interest, offering holistic monitoring by the centralized SOC. The incorporation of the centralized SOC in the overall architecture allows also the centralized application of machine learning algorithms on all the received data. Full article

In complex electromagnetic environments, wireless communication system reliability can be compromised by various types of jamming. To improve reliability in the presence of malicious jamming, this paper introduces an anti-jamming power control algorithm for wireless communication systems, grounded in model predictive control (MPC) principles. The algorithm models the Yescommunication system as a linear control system, using the current signal-to-jamming-and-noise ratio (SJNR) to predict future system states and transmission power over a defined time horizon. Only the first element of the optimal control sequence is then applied to manage system power. Simulation results indicate that, compared to traditional adaptive power control algorithms, the proposed algorithm responds more swiftly to jamming variations, significantly enhancing communication reliability in high-jamming environments. Full article

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not. Moreover, to develop a trade-off between the achievable performance and implementation complexity, we propose both element-wise and group-wise reflected perturbation alignment (ERPA/GRPA)-based IRS reflection strategies, respectively. In order to evaluate the achievable performance, we analyze the ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of the SISO secure systems with the ERPA/GRPA-based JPRs, respectively. Finally, by characterizing the simulated and numerical ESR and SOP performance results, our proposed scheme is compared with the benchmark scheme of random phase-based reflection, which strongly demonstrates the effectiveness of our proposed scheme. Full article