Search Results (304)

Fingertrap is a network encryption and authentication protocol that extends the X3DH and Double Ratchet frameworks with three novel mechanisms inspired by the Chinese finger trap (zhĭ wăng): a friction ratchet that exponentially increases computational cost for each failed authentication attempt; a recursive annihilation protocol that irreversibly destroys all cryptographic state after a configurable failure threshold; and a commit-then-challenge handshake that requires a counterintuitive “inward” action for legitimate authentication. A bidirectional weave hash extends the Double Ratchet’s transcript binding to cover every message in both directions. Together, these mechanisms provide per-message forward secrecy, post-compromise security (self-healing), clock-free operation, and a self-destruct capability. The individual ingredients-client puzzles, key erasure, and ratcheting-each build on established lines of work; their combination into a single stateful protocol, in which failed authentication attempts cryptographically tighten the session state and ultimately destroy it, is not to our knowledge offered by deployed transport protocols such as TLS 1.3, Signal, or WireGuard. The design targets deployments in which interception or capture of a device implies endpoint compromise, such as Unmanned Aerial Vehicle (UAV) telemetry links and body-worn sensors, where denial of exploitation requires guaranteed loss of past and future session material. We describe the full protocol, provide game-based security arguments under an explicit adversarial model, give analytic cost estimates for the friction mechanism, analyse the denial-of-service surface and a two-layer mitigation strategy, and specify a post-quantum extension using hybrid $\mathsf{X25519}\text{/}\mathsf{ML}\text{-}\mathsf{KEM}\text{-}\mathsf{768}$ ratcheting. Full article

Signalling traffic in 5G Interconnect and Roaming Networks (IRNs) over the IP eXchange (IPX) faces man-in-the-middle (MITM) interception on N32 and GTP-U, billing fraud, and “Harvest Now, Decrypt Later” (HNDL) adversaries. This paper develops and validates qSiP, a hybrid quantum–classical framework integrating Quantum Key Distribution (QKD, BB84 decoy-state protocol) and Quantum Secure Direct Communication (QSDC, DL04) at OSI Layers 1–2 with a classical-plus-quantum ML classifier for signalling anomaly and billing-fraud detection. Evaluation spans NS-3, Mininet, and MicroK8s on a dual-PLMN Open5GS testbed with PacketRusher NB-IoT traffic and STRIDE-L threat modelling. Cryptographically, qSiP holds BB84 QBER within decoy-state thresholds, sustains key rates matched to N32 timing, and under HNDL conditions bounds adversary exposure to one key-rotation interval; N32 captures confirm 3GPP message format and handshake semantics end-to-end. For detection, the Hybrid configuration reaches 98.5–99.5% accuracy across environments (vs. 89.4–96.0% classical), reduces undetected billing fraud over four roaming paths, and adds under 5 ms latency per signalling exchange. Statistical tests confirm hybrid > classical at p < 0.05 across eMBB, uRLLC, mMTC, and BCE. The work contributes an integrated framework with simulation-based empirical validation in a standards-aligned 5G SA testbed and a methodological commitment for future quantum-roaming research: quantum ML detects but does not encrypt, while QKD/QSDC encrypt does not classify; these are two complementary, non-interchangeable roles. Full article

Low Power Wide Area Networks (LPWANs) are essential for enabling Internet-of-Things (IoT) technologies in utility environments. Utilities can leverage these networks to monitor critical remote assets, especially where mobile technologies are unsuitable due to poor power efficiency or insufficient coverage. This paper investigates the use of Long Range (LoRa) Wide Area Network (LoRaWAN) technology as an LPWAN solution for remote grid monitoring within the eThekwini Municipal Area. In addition to evaluating range performance (distance) and the packet reception ratio (PRR) across configurable parameters, such as spreading factor and transmit power, this paper introduces a data-packet security extension for LoRaWAN using NTRU post-quantum cryptography (PQC). The proposed security enhancement provides quantum-resistant encryption for application-layer payloads without violating LoRaWAN duty-cycle constraints or significantly increasing energy consumption. Field tests were performed at 11 geographically dispersed substations using a handheld LoRa device. Test signals were transmitted at four power levels (2 dBm, 8 dBm, 14 dBm, and 20 dBm) and spreading factors (SF7–SF12). Results show that the public LoRaWAN network can achieve communication distances of approximately 30 km in an urban environment, with PRR strongly dependent on SFs and transmit power. The integration of lightweight NTRU-protected payloads was found to be feasible for typical smart grid use cases involving small data packets (1–13 bytes). Full article

Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering substantial efficiency gains by prioritizing the transmission of task-relevant meaning over bit-level accuracy. However, while its benefits in bandwidth reduction and intelligent data representation are well established, its potential to provide intrinsic reconstruction resistance without relying on conventional cryptographic mechanisms remains largely unexplored. This paper investigates whether semantic communication system architectures themselves can contribute to intrinsic reconstruction resistance for image transmission. We propose an autoencoder-based semantic communication framework in which images are encoded into latent representations and transmitted over a wireless channel, with decoding performed using architecture-specific neural networks. Unlike traditional secure communication approaches that depend on encryption, the proposed method leverages architectural uniqueness and representation-level abstraction to limit unauthorized reconstruction. To systematically analyze this, we evaluate eight adversarial scenarios encompassing variations in encoder–decoder architecture and initialization, including both matched (worst-case) and maximum mismatched (best-case) conditions. The system is modeled using a standard Alice–Bob–Mallory framework, where an adversary attempts to reconstruct intercepted semantic representations without full architectural knowledge. Performance is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for reconstruction quality, alongside semantic accuracy measured via a convolutional neural network (CNN)-based classifier and embedding cosine similarity to assess information leakage. Experimental results demonstrate that architectural mismatches substantially degrade both visual reconstruction and semantic interpretability for unauthorized receivers, while matched configurations enable substantial recovery. It is important to emphasise that the proposed approach does not provide cryptographic confidentiality; rather, it offers architecture-dependent resistance to unauthorised semantic reconstruction under restricted adversarial assumptions. Overall, the results show that semantic communication systems can exhibit intrinsic reconstruction resistance through architecture-dependent latent-space organisation, reducing reliance on additional cryptographic overhead under restricted adversarial assumptions, while also highlighting limitations when adversaries possess full architectural and initialisation knowledge. Full article

Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into $64\times 9$ grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications. Full article

With the emergence of mobile sixth-generation (6G) integrated sensing and communication (ISAC) scenarios, conventional multicarrier waveforms face challenges in maintaining reliable communication and robust physical-layer security. In this paper, we propose a multi-layer encryption multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) with index modulation (IM) scheme, which exploits the inherent flexibility of the AFDM modulation parameter $c_2$ and subcarrier IM to construct a multi-dimensional physical-layer security mechanism. To enable sensing and exploit MIMO spatial diversity, a unified downlink MIMO configuration is adopted, where sensing and communication share the same transmit waveform, receive array, and physical propagation environment. The proposed configuration enables multi-dimensional parameter estimation, including delay, Doppler, and angle. The obtained sensing information further assists beamforming design, channel reconstruction, and signal equalization. Furthermore, the base station and user equipment share synchronized secret keys, and a unified detection framework is developed to balance computational complexity and detection accuracy while remaining compatible with the multi-dimensional encryption structure of the MIMO-AFDM-IM system. Simulation results verify the effectiveness of the proposed scheme in mobile scenarios, demonstrating enhanced multi-dimensional sensing accuracy, improved resistance to eavesdropping, and superior communication reliability and energy efficiency (EE). Full article

Although the sharing of data is an important part of multicenter biomedical AI, direct data sharing is hindered by privacy laws, institutional data silos, and restrained trust and cooperation between institutions. While federated learning offers an opportunity for collaborative model training without centralizing patient data, many current methods rely on the same fixed levels of privacy protection on all clients, every layer of the model, each round, and each modality, resulting in suboptimal privacy–utility–latency trade-offs. In this study, we introduce Adaptive Trust-Aware Encrypted Federated Artificial Intelligence with Blockchain Auditability (ATEB-AI) for biomedical signal and medical image analysis. ATEB-AI is an adaptive CKKS encryption, trust-aware aggregation, and permissioned blockchain-based audit logging combination. The proposed framework was tested on four public benchmarks, namely, MIT-BIH, CHB-MIT, BraTS, and NIH ChestXray. ATEB-AI had the highest overall performance out of all compared federated methods and remained near the centralized training benchmark at up to 99.0% of the reference centralized training performance. It reduced membership-inference success from 0.71 to 0.24 (−66.2%), inversion leakage from 0.64 to 0.27 (−57.8%), and poisoning-related utility loss from 0.18 to 0.07 (−61.1%). Round latency was 1.90× FedAvg, compared with 2.85× for HE-FL (−33.3%) and 3.50× for BC-FL (−45.7%). The key contribution of this study is a single biomedical federated learning framework in which privacy, client trust, reliability, and auditability are unified, instead of being disjointed components. The results obtained with the proposed model prove the feasibility of co-optimizing confidentiality, robustness, efficiency, and governance in a single deployable multicenter medical AI pipeline. Full article

To further enhance the covert communication capability of satellite–ground-integrated sensor networks, a dual-polarization constellation joint modulation scheme based on frequency-hopping double-layer multi-parameter weighted fractional Fourier transform (FH-DL-MPWFRFT) is proposed from the perspective of physical layer security. The proposed scheme integrates the constellation confusion property of weighted fractional Fourier transform (WFRFT) with the anti-interception capability of frequency-hopping (FH) phase scrambling. Specifically, the weighted parameters of conventional 4-WFRFT are extended to construct a multi-parameter and multi-layer signal representation, and FH phase scrambling is introduced to realize dynamic constellation rotation and phase-domain encryption. Furthermore, a secure transmission model for satellite–ground-integrated sensor networks is established, revealing the constellation optimization principle and the fission-fusion mechanism of dual-polarization signals. Simulation results show that, compared with the non-FH benchmark, the proposed scheme significantly improves waveform-level anti-interception performance; even when eavesdropper obtains the modulation scheme and partial transform parameters, the symbol error rate (SER) of quadrature phase shift keying (QPSK) and four-phase modulation (4PM) signals remains around 0.4 to 0.5 under parameter mismatch, indicating that effective demodulation is difficult to achieve. Full article

Modern vehicles rely on in-vehicle network protocols such as Controller Area Network (CAN) protocol, but these protocols were designed without encryption or authentication. Therefore, the vehicles are exposed to cyber attacks. Motion-based Intrusion Detection Systems (MIDSs) exploit correlation between physically related signals to detect attacks. However, we show that MIDSs are vulnerable, because correlation coefficient is invariant to positive linear scaling. Hence, an adversary may manipulate a signal while keeping its correlation high. In this paper, we propose a Correlation Scaling Attack (CSA) that forges wheel speed signals by scaling their original value while keeping the temporal trend consistent with the other signal. We analyze that correlation coefficient remains unchanged when the signal is forged. Consequently, the CSA evades conventional MIDSs. To mitigate this limitation of MIDS, we exploit covariance between two signals as a complementary indicator, since covariance provides magnitude information. We evaluate the proposed attack and defense mechanism using CAN log data collected from a real vehicle. Experimental results verify the effectiveness of CSA, and we demonstrate that CSA can be detected by observing covariance between two signals. Our research not only indicates that the CSA is a significant threat to cars, but provides a feasible mitigation exploiting the covariance. Full article

With the increasing demand for secure information dissemination, image privacy protection has become an important research direction. This study proposes an image encryption algorithm based on reversible quantum gate computation for secure image protection. The proposed method is implemented on an FPGA platform to realize quantum gate operations for encrypting plaintext images in real time and storing the encrypted images on an SD card. The decryption of the encrypted image by reversible quantum gate computation is expected to provide a new method for real-time image encryption. Experimental results demonstrate that the entropy analysis shows values meet a certain standard in terms of image steganography and security, with an entropy value close to the maximum value of 8. In addition, the Peak Signal-to-Noise Ratio (PSNR) value of the decrypted image is also more than 30 dB, which indicates that the proposed image encryption system can effectively maintain the image quality. Full article

Secure real-time communication is a critical requirement in modern electric vehicle (EV) networks. These networks transmit safety-critical control commands through vulnerable in-vehicle communication channels. This study proposes a novel three-dimensional symmetric chaotic system for high-security EV communication. The system exhibits extensive multistability and symmetric double-wing attractors. To enhance dynamical complexity, its parameters are optimized using chaotic-enhanced particle swarm optimization (C-PSO). The largest Lyapunov exponent is used as the optimization objective. A fixed-time nonlinear controller is designed for rapid drive–response synchronization. The settling-time bound is independent of the initial conditions. The proposed method is evaluated through realistic Controller Area Network (CAN) bus simulations. These simulations include 12-bit quantization and a 1 ms sampling period. The experimental results show synchronization within 0.057 s. The recovered signal achieves an MSE of $1.202\times {10}^{-4}$. The encrypted signal reaches a Shannon entropy of 7.9904. These results confirm accurate recovery, strong randomness, and improved resistance to cryptographic attacks. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article

Ultrafast physical random bit generators (PRBGs) are essential components for modern applications in secure communication, quantum cryptography, encrypted optical fiber sensing and artificial intelligence. While optical chaos-based PRBGs offer high-speed capabilities, conventional systems often rely on discrete components that suffer from system complexity and environmental instability. This paper proposes and experimentally demonstrates a robust, integrated solution using a two-section mutual injection DFB laser. The device was fabricated using the reconstruction equivalent chirp (REC) technique, which provides precise control over grating phase variation while utilizing low-cost, high-volume fabrication methods. The laser sections, each measuring 450 μm in length, were designed with a free-running wavelength difference of 0.3 nm to ensure a flat optical spectrum and enhanced chaotic dynamics. By optimizing the bias currents, we achieved a chaos RF bandwidth of 20.1 GHz. Notably, the resulting chaotic signal lacks time-delayed signatures, which simplifies the randomness extraction process. To generate random bits, the chaotic waveform was sampled by an 8-bit analog-to-digital converter at 100 GSa/s. Following post-processing through delay-subtracting and the extraction of the four least significant bits (4-LSBs), we realized a total physical random bit rate of 400 Gb/s. The randomness of the generated sequence was successfully verified using the NIST SP 800-22 statistical test suite. This approach offers a compact, energy-efficient, and high-performance integrated chaotic source suitable for secure communication and high-performance computation. Full article

This paper extends the theory of equivalence canonical forms from quaternion matrices to quaternion tensors under the Einstein product. Motivated by recent results on the simultaneous decomposition of two specific configurations of five quaternion matrices, we establish a comprehensive framework for the corresponding configurations of five quaternion tensors. The core approach leverages bijective transformation maps that establish isomorphisms between quaternion tensor spaces and matrix spaces, allowing us to systematically construct invertible transformation tensors that simultaneously reduce the given tensor quintuples to canonical forms consisting solely of binary entries (0 and 1). A detailed structural analysis of the resulting canonical tensor forms is provided, including explicit dimension formulas for all identity blocks derived from precise rank conditions. To demonstrate practical utility, we integrate the proposed tensor decomposition with the discrete wavelet transform to construct a color video encryption and decryption system. Experimental results confirm perfect reconstruction (PSNR exceeding 300 dB, SSIM equal to 1) and strong security performance: NPCR of 49.8%, UACI of 49.6%, information entropy of 0.9986 bits per pixel, adjacent pixel correlation below 0.03 in absolute value, and a key space exceeding 2⁵¹². The developed theory significantly extends the existing literature on quaternion tensor decompositions and provides powerful tools for multidimensional signal processing. Full article

Wireless sensor networks (WSNs) have increasing demand on lightweight, efficient, and secure encryption techniques for devices with limited resources, since traditional algorithms require high computation which make them impractical. This preliminary study presents an encryption algorithm based on chaos designed for transmitting short data, using the Lorenz system and Euler’s method for computation. It is combined with a synchronization model based on data array. It inserts iteration parameters within the ciphertext to ensure consistent key reproduction while decrypting. Within the broader context of e-health data streams, encryption efficiency is critical: continuous ECG signals generate large volumes of data that challenge real-time secure transmission, whereas individual blood pressure readings are far smaller and lightweight. While this work delimits its scope to short, low-power transmissions, simulations and hardware implementation on an nRF chip using the Enhanced ShockBurst (ESB) protocol demonstrated efficiency, with the lowest encryption speed of 0.154 ms for a 1-byte payload. Security analysis using the NIST Statistical Test Suite confirmed high statistical randomness of the generated keystream, and theoretical key-space analysis supports robustness. By focusing on short-stream encryption in preliminary form, the scheme contributes toward inclusive secure communication technologies for resource-constrained IoT healthcare systems and diverse user populations. Full article