Search Results (291)

Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems. Full article

Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article

Secure communication is critically dependent on high-speed and high-security quantum random number generation (QRNG). In this work, we present a responsive approach to enhance the efficiency and security of QRNG by leveraging polarization-controlled heterodyne detection to simultaneously measure the quadrature amplitude and phase fluctuations of vacuum shot noise. To address the practical non-idealities inherent in QRNG systems, we investigate the critical impacts of imbalanced heterodyne detection, amplitude–phase overlap, finite-size effects, and security parameters on quantum conditional min-entropy derived from the entropy uncertainty principle. It effectively mitigates the overestimation of randomness and fortifies the system against potential eavesdropping attacks. For a high-security parameter of ${10}^{-20}$, QRNG achieves a true random bit extraction ratio of 83.16% with a corresponding real-time speed of 37.25 Gbps following a 16-bit analog-to-digital converter quantization and 1.4 GHz bandwidth extraction. Furthermore, we develop a deep convolutional neural network for rapid and accurate entropy evaluation. The entropy evaluation of 13,473 sets of quadrature data is processed in 68.89 s with a mean absolute percentage error of 0.004, achieving an acceleration of two orders of magnitude in evaluation speed. Extracting the shot noise with full detection bandwidth, the generation rate of QRNG using dual-quadrature heterodyne detection exceeds 85 Gbps. The research contributes to advancing the practical deployment of QRNG and expediting rapid entropy assessment. Full article

To combat microphone eavesdropping on devices like smartphones, ultrasonic-based methods offer promise due to human inaudibility and microphone nonlinearity. However, existing systems suffer from low jamming efficiency, poor energy utilization, and weak robustness. Based on these problems, this paper proposes a novel ultrasonic-based jamming algorithm called the Time–Frequency Mosaic (TFM) technique, which can be used for anti-eavesdropping. The proposed TFM technique can generate short-time, frequency-coded jamming signals according to the voice frequency characteristics of different speakers, thereby achieving targeted and efficient jamming. A jamming prototype using the Time–Frequency Mosaic technique was developed and tested in various scenarios. The test results show that when the signal-to-noise ratio (SNR) is lower than 0 dB, the text Word Error Rate (WER) of the proposed method is basically over 60%; when the SNR is 0 dB, the WER of the algorithm in this paper is on average more than 20% higher than that of current jamming algorithms. In addition, when the jamming system maintains the same distance from the recording device, the algorithm in this paper has higher energy utilization efficiency compared with existing algorithms. Experiments prove that in most cases, the proposed algorithm has a better jamming effect, higher energy utilization efficiency, and stronger robustness. Full article

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article

The Internet of Drones (IoD) overcomes the physical limitations of traditional ground networks with its dynamic topology and 3D spatial flexibility, playing a crucial role in various fields. However, eavesdropping and spoofing attacks in open channel environments threaten data confidentiality and integrity, posing significant challenges to IoD communication. Existing foundational schemes in IoD primarily rely on symmetric cryptography and digital certificates. Symmetric cryptography suffers from key management challenges and static characteristics, making it unsuitable for IoD’s dynamic scenarios. Meanwhile, elliptic curve-based public key cryptography is constrained by high computational complexity and certificate management costs, rendering it impractical for resource-limited IoD nodes. This paper leverages the low computational overhead of Chebyshev polynomials to address the limited computational capability of nodes, proposing a certificateless public key cryptography scheme. Through the semigroup property, it constructs a lightweight authentication and key agreement protocol with identity privacy protection, resolving the security and performance trade-off in dynamic IoD environments. Security analysis and performance tests demonstrate that the proposed scheme resists various attacks while reducing computational overhead by 65% compared to other schemes. This work not only offers a lightweight certificateless cryptographic solution for IoD systems but also advances the engineering application of Chebyshev polynomials in asymmetric cryptography. Full article

The massive use of end information in the end hopping system not only significantly improves the proactive defense capability but also reveals great potential for covert communication. However, the development of existing network covert channels is hindered by various elimination techniques and a lack of robustness guarantees. In this paper, we first present a novel network covert channel model based on end spreading (CCES) in the end hopping system. We then propose a CCES-based scheme using m-sequence in the hypothetical scenario and theoretically analyze its characteristics, including eavesdropping resistance, loss tolerance, and robust synchronization. To evaluate the performance of the CCES scheme, three evaluation metrics are adopted: non-detectability, robustness, and transmission efficiency. Experimental results show that CCES achieves a bit error rate (BER) below 5% under 30% packet loss, entropy values ranging from 0.15 to 0.82 (comparable to normal traffic), and a transmission efficiency of up to 800 bits per second. These results confirm the CCES scheme’s strong robustness and practical applicability, outperforming traditional covert channels in both reliability and stealth. Full article

Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of surveillance by sending interference signals to suspicious receivers, which is important for crime prevention and public safety. In this paper, we investigate the joint optimization of performance of both ISAC and active surveillance. Specifically, we formulate a bilevel optimization problem where the upper-level objective aims to maximize the probability of successful eavesdropping while the lower-level objective aims to optimize the localization performance of the radar on suspicious transmitters. By employing the Rayleigh quotient, introducing a decoupling strategy, and adding penalty terms, we propose an algorithm to solve the bilevel problem where the lower-level objective is convex. With the help of the proposed algorithm, we obtain the optimal solution of the analog transmit beamforming matrix and the digital beamforming vector. Performance analysis and discussion of key insights, such as the trade-off between eavesdropping success probability and radar localization accuracy, are also provided. Finally, comprehensive simulation results validate the effectiveness of our proposed algorithm in enhancing both the eavesdropping success probability and the accuracy of radar localization. Full article

With the increasing demand for drones in diverse tasks, the Internet of Drones (IoD) has recently emerged as a significant technology in academia and industry. The IoD environment enables various services, such as traffic and environmental monitoring, disaster situation management, and military operations. However, IoD communication is vulnerable to security threats due to the exchange of sensitive information over insecure public channels. Moreover, public key-based cryptographic schemes are impractical for communication with resource-constrained drones due to their limited computational capability and resource capacity. Therefore, a secure and lightweight key agreement scheme must be developed while considering the characteristics of the IoD environment. In 2024, Alzahrani proposed a secure key agreement protocol for securing the IoD environment. However, Alzahrani’s protocol suffers from high computational overhead due to its reliance on elliptic curve cryptography and is vulnerable to drone and mobile user impersonation attacks and session key disclosure attacks by eavesdropping on public-channel messages. Therefore, this work proposes a lightweight and security-enhanced key agreement scheme for the IoD environment to address the limitations of Alzahrani’s protocol. The proposed protocol employs a physical unclonable function and simple cryptographic operations (XOR and hash functions) to achieve high security and efficiency. This work demonstrates the security of the proposed protocol using informal security analysis. This work also conducted formal security analysis using the Real-or-Random (RoR) model, Burrows–Abadi–Needham (BAN) logic, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation to verify the proposed protocol’s session key security, mutual authentication ability, and resistance to replay and MITM attacks, respectively. Furthermore, this work demonstrates that the proposed protocol offers better performance and security by comparing the computational and communication costs and security features with those of relevant protocols. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. 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

Quantum information transmission is subject to imperfections in communication processes and systems. These phenomena alter the original content due to decoherence and noise. However, suitable communication architectures incorporating quantum and classical redundancy can selectively remove these errors, boosting destructive interference. In this work, a selection of architectures based on path superposition or indefinite causal order were analyzed under appropriate configurations, alongside traditional methods such as classical redundancy, thus enhancing transmission. For that purpose, we examined a broad family of decoherent channels associated with the qubit chain transmission by passing through tailored arrangements or composite architectures of imperfect channels. The outcomes demonstrated that, when combined with traditional redundancy, these configurations could significantly improve the transmission across a substantial subset of the channels. For quantum key distribution purposes, two alternative bases were considered to encode the information chain. Because a control system must be introduced in the proposed architectures, two strategies for its disposal at the end of the communication process were compared: tracing and measurement. In addition, eavesdropping was also explored under a representative scenario, to quantify its impact on the most promising architecture analyzed. Thus, in terms of transmission quality and security, the analysis revealed significant advantages over direct transmission schemes. Full article

In the era of ubiquitous big data, ensuring secure storage, transmission, and processing has become a paramount concern. Classical cryptographic methods face increasing vulnerabilities in the face of quantum computing advancements. This research proposes an enhanced big data security framework integrating a quantum chaotic map of key sequence (QCMKS), which synergizes the principles of quantum mechanics and chaos theory to generate highly unpredictable and non-repetitive key sequences. The system incorporates quantum random number generation (QRNG) for true entropy sources, quantum key distribution (QKD) for secure key exchange immune to eavesdropping, and quantum error correction (QEC) to maintain integrity against quantum noise. Additionally, quantum optical elements transformation (QOET) is employed to implement state transformations on photonic qubits, ensuring robustness during transmission across quantum networks. The integration of QCMKS with QRNG, QKD, QEC, and QOET significantly enhances the confidentiality, integrity, and availability of big data systems, laying the groundwork for a quantum-resilient data security paradigm. While the proposed framework demonstrates strong theoretical potential for improving big data security, its practical robustness and performance are subject to current quantum hardware limitations, noise sensitivity, and integration complexities. Full article

We consider the scenario in which the source sends status updates, or packets, to the receiver through an infinite capacity transmitter, where the transmitted packets are subject to potential illegal eavesdropping. Time is discretized into identical time slots. In recent years, the age of information (AoI) metric, which was defined as the time has elapsed since the generation instant of the latest received packet, has been widely applied to characterize the freshness of obtained packets. Due to the presence of eavesdroppers, some packets may be eavesdropped during their transmissions, causing information leakages. To assess an infinite-capacity system’s performance of securely transmitting status updates, in this paper, we define an AoI-related metric called the freshness advantage of the legitimate receiver, F, to be average instantaneous gap between eavesdropper’s and legitimate receiver’s AoI. For arbitrarily distributed packet interarrival times, and assuming that in each time slot with probabilities ${\gamma }_d$, ${\gamma }_E$, the transmitted packet is received by the legitimate receiver and the eavesdropper, we derive the explicit formula of F. The concise expression shows that F is fully determined by the average interarrival time and the ratio of ${\gamma }_d$ to ${\gamma }_E$. For special cases where the interarrival time follows geometric distributions, we first determine the explicit distribution of instantaneous AoI gap. Then, given ${\gamma }_d$ and ${\gamma }_E$, we derive the optimal packet generation rate $p^{\ast }$ that minimizes the combined performance Q, which is constructed as the average AoI minus the freshness advantage F. When imposing timeliness and security constraints at the same time, the feasible regions of p and ${\gamma }_d$ such that both two required performances can be satisfied are depicted and discussed. Finally, we investigate the impacts of different parameters on F and show the tradeoffs between timeliness performance and security performance through numerical simulations. Full article

As an important part of the Internet of Things, the Internet of Vehicles (IoV) has achieved efficient interconnection and collaboration between vehicles and road infrastructure, and between vehicles through advanced information and communication technologies. However, the high-speed movement of vehicles has generated a large number of cross-domain behaviors, which has greatly increased the number of authentications. Existing authentication protocols face challenges such as high cost, high computational overhead, and easy eavesdropping, interception, or tampering. To this end, this paper proposes an ECC-based IoV secure and efficient handover authentication protocol. The protocol adopts a “non-full key escrow” mechanism. The private key of the vehicle is jointly generated by the Trusted Authority (TA) and the vehicle. The TA only holds part of the private key. Even if the TA is malicious, the security of the vehicle’s private key can be ensured. At the same time, the proposed protocol uses the time tree technology in trusted computing to share part of the vehicle’s private data, which not only ensures the security of authentication, but also improves the efficiency of authentication, and solves the high-latency problem caused by the use of blockchain in previous protocols. When the vehicle moves across domains, there is no need to re-register and authenticate, which reduces the authentication overhead. Compared with existing protocols, this protocol is lightweight in both computational and communication overheads, effectively solving the problem of excessive cost. Full article