Search Results (543)

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the `encoding’ and interpretation of the dynamic `vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

With the increasing demand for earth observation and communication missions, semi-rigid airships have emerged as critical aerial platforms due to their long endurance and high payload capacity. However, high-precision dynamic modeling and robust autonomous flight control remain challenging because of large hull volume and strong aerodynamic nonlinearities. This study proposes an integrated framework combining computational fluid dynamics (CFD) aerodynamic modeling with full-envelope gain scheduling control. First, nonlinear aerodynamic characteristics over wide ranges of angles of attack and sideslip are identified via CFD simulation, and a six-degree-of-freedom (6-DOF) nonlinear dynamic model incorporating added-mass effects is established. Subsequently, a gain scheduling linear quadratic regulator (LQR) controller is then designed using airspeed, climb rate, and yaw rate as scheduling variables, enabling coordinated control allocation between low-speed thrust vectoring and high-speed aerodynamic surfaces. Simulation results demonstrate improved three-dimensional (3D) path following performance and smooth flight mode transitions. The mean absolute errors (MAEs) in altitude, airspeed, and heading are limited to 0.711 m, 0.028 m/s, and 2.377°, respectively. Furthermore, the system’s robustness is validated under composite wind disturbances, confirming effectiveness of the proposed approach across the full flight envelope. Full article

This work develops a zero-trust, physics-constrained mathematical framework for detecting stealthy drift perturbations in power system dynamical models. Such perturbations constitute adversarial, statistical deviations that preserve first-order operating trends, making them difficult to identify using classical residual-based estimators or unconstrained data-driven models. We introduce ZETWIN, a spatio-temporal learning architecture formulated as a constrained optimization problem in which the nodal admittance matrix $Y_{bus}$ acts as a graph-structured linear operator embedded directly into the loss functional. This construction enforces Kirchhoff-consistent latent representations and yields a mathematically grounded zero-trust decision rule that flags any trajectory violating physical feasibility, independent of prior attack signatures. The proposed framework is evaluated using a PyPSA-based AC–DC meshed network, demonstrating an AUROC = 0.994, and F1 = 0.969. The formulation highlights how physics-informed constraints, graph operators, and spatio-temporal approximation theory can be combined to construct mathematically interpretable zero-trust detectors for complex dynamical systems. Full article

Physical Unclonable Functions (PUFs) represent a highly promising hardware security primitive, yet they face constraints of insufficient reliability and threats from modeling attacks. This paper designs a novel Feed-Forward Crossbar Matrix Arbiter PUF (FC-MA PUF). It incorporates an inter-stage crossbar structure, a feed-forward control system, and a mechanism for selecting reliable challenge-response pairs. These features significantly enhance the structural non-linearity and stability, substantially improving security and adaptability to a wider range of operating environments. It provides a high-strength authentication solution with low resource overhead for lightweight security-demanding devices such as IoT devices. The proposed FC-MA PUF has been successfully implemented on a Field-Programmable Gate Array (FPGA) platform. Experimental results for the selected 4-stage FC-MA PUF configuration show a bias, inter-chip uniqueness, and bit error rate (BER) of 49.88%, 49.68%, and 0.018%, respectively. Furthermore, the structure allows for flexible configuration of the number of feed-forward modules based on practical application requirements: a greater number of feed-forward modules enhances security but also leads to an increased BER and a decreased proportion of stable challenge-response pairs. Experimental results based on a training set of 1,000,000 challenge-response pairs demonstrate that: with two feed-forward units, the stable (Challenge Response Pair)CRP ratio is 39.72% and the Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) attack prediction success rate is 58.20%; with three units, the ratio decreases to 29.12% and the prediction rate drops to 54.91%; with four units, these values further decline to 20.18% and 52.33% respectively. These results confirm that the proposed FC-MA PUF effectively resists multiple modeling attacks, including Logistic Regression (LR), Support Vector Machine (SVM), and CMA-ES. Full article

A recent study of World Trade Center Health Registry enrollees found that about one- third experienced post-traumatic growth (PTG) in the wake of the 9/11 attacks and that PTG was associated with social support and social integration. However, the implications of PTG for the enrollees’ overall quality of life are unknown. The present study investigated the prevalence of PTG and its association with the SF-12 physical and mental function quality of life scales in a sample of 2786 enrollees from the Registry’s Health and Quality of Life Study (HQoL) who completed the first four surveys, were older than 18 on 9/11, reported English as their primary spoken language, and provided consistent self-report of 9/11 physical injury at the Registry’s baseline and HQoL surveys. We employed multivariable linear regression to evaluate the association between PTG and the SF-12 physical and mental scales, controlling for sex, age, race, education, income, employment, social support, threatening events, post-9/11 mental health, number of post-9/11 physical health conditions, and drug/alcohol misuse. We found that 31% of the sample enrollees experienced PTG and that PTG exhibited a clinically and statistically significant association with the SF-12 mental scale but not the physical scale (physical: β = −0.01 (−0.61, 0.65), mental: β = 3.92 (2.89, 4.95)). Those who were physically injured during 9/11 showed larger improvements in mental function than those who were not. PTG has implications for the overall mental quality of life that should be further investigated. Full article

Generative adversarial networks (GANs) are increasingly applied to mitigate extreme class imbalance in intrusion detection systems, yet reported improvements often obscure role augmentation intensity and adversarial stability. This paper presents a controlled ablation study that isolates the impact of adversarial objective choice, augmentation ratio, and training duration on GAN-based minority data augmentation for highly imbalanced tabular cybersecurity data. Using the UWF-ZeekData22 dataset, nine MITRE ATT&CK tactic-versus-benign classification tasks are evaluated under augmentation ratios of 0.25 and 0.50 and training durations of 400 and 800 epochs. Four GAN variants—Vanilla GAN, Conditional GAN (cGAN), WGAN, and WGAN-GP—are assessed using stratified cross-validation and five classical classifiers representing diverse inductive biases. The results reveal consistent structural patterns. Moderate augmentation (r = 0.25) with controlled training (400 epochs) yields the most stable and reliable improvement in minority recall. Wasserstein-based objectives demonstrate superior stability under aggressive augmentation and prolonged training, while conditional GANs frequently exhibit recall collapse in ultra-sparse regimes. Increasing augmentation volume does not uniformly improve performance and may introduce distributional overlaps that degrade linear and margin-based classifiers. Tree-based classifiers remain largely invariant once sufficient minority density is achieved. These findings demonstrate that adversarial calibration is more important than architectural complexity for improving the detection of rare attacks. The study provides practical guidance for designing robust GAN-based augmentation pipelines under extreme cybersecurity class imbalance. Full article

Targeting resource-constrained Internet of Things (IoT) devices, this paper proposes Stuck-at-Fault Physical Unclonable Function (SAF-PUF), a lightweight Resistive Random-Access Memory (RRAM)-based PUF that exploits the intrinsic addresses of manufacturing-induced SAF defects as a stable entropy source. By using the coordinates of Stuck-at-1 (SA1) cells to seed a 32-bit Linear Feedback Shift Register (LFSR), SAF-PUF generates robust, variable-length responses with zero Bit Error Rate (BER) across a wide temperature range from −40 °C to 125 °C, without any error-correction circuitry. Experimental results based on 100,000 Challenge–Response Pairs (CRPs) demonstrate strong resilience against machine learning (ML) attacks, with prediction accuracies of logistic regression (LR), support vector machines (SVM), neural networks (NN) and convolutional neural networks (CNNs) remaining close to 50%. Moreover, a “use-then-conceal” mechanism is introduced to enhance post-authentication security, enabling response obfuscation with minimal cell reconfiguration. These features make SAF-PUF a high-security, low-overhead hardware root of trust suitable for IoT applications. Full article

Developed in 2023, the Modified Generalized Feistel Network (MGFN) is a block cipher that complies with Malaysia’s national cryptographic and cybersecurity policies. MGFN is a 64-bit block cipher with a 128-bit master key, specifically designed to deliver lightweight cybersecurity in resource-constrained Internet of Things (IoT) environments. In this paper, we analyze the security of the full-round MGFN against differential and linear cryptanalysis. We present concrete key recovery strategies for both attacks by employing multiple peeling-off steps. As a result, for the first time, we demonstrate a practical differential cryptanalysis of the full-round MGFN within a realistic time bound. In addition, we propose a practical linear cryptanalysis of the round-reduced MGFN. Our results provide the first practical security assessment of MGFN and offer concrete insights into its resistance against differential and linear cryptanalysis, thereby supporting the design and evaluation of lightweight block ciphers for IoT environments. Full article

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience. Full article

The Internet of Things (IoT) faces critical security challenges stemming from resource-constrained devices and inadequate intrusion detection capabilities. Traditional machine learning approaches struggle with high-dimensional network traffic data due to the curse of dimensionality, severe class imbalance between benign and malicious traffic, and dependence on manual feature engineering that fails to capture complex non-linear attack patterns. Although deep neural networks offer automatic feature extraction, they suffer from two fundamental limitations: the degradation problem, where increasing network depth paradoxically raises training error rather than improving performance, and uniform channel weighting, which prevents the network from adaptively emphasizing attack-relevant features while suppressing irrelevant noise. This research proposes a novel hybrid framework integrating Fractional Tchebichef moment-based feature preprocessing with deep Residual Networks enhanced by Squeeze-and-Excitation (ResNet-SE) attention mechanisms. Fractional Tchebichef moments provide compact, noise-resistant representations by operating directly in the discrete domain, eliminating discretization errors inherent in continuous moment approaches. Network traffic features are transformed into 232 × 232 moment-based matrices capturing discriminative patterns across multiple scales. Comprehensive evaluation on Bot-IoT and Leopard Mobile IoT datasets demonstrates superior performance, achieving 99.78% accuracy and a 99.37% F1-score, substantially outperforming K-Nearest Neighbors (84.7%), Support Vector Machines (87.5%), and baseline CNNs (99.3%). Ablation studies confirm synergistic contributions, with residual connections contributing 0.18% and SE attention adding 0.14% improvements. Cross-dataset evaluation achieves 96.34% and 97.12% accuracy on UNSW-NB15 and IoT-Bot datasets without retraining, while the framework processes 127.9 samples per second across diverse attack taxonomies. Full article

This paper addresses the stability and $H_{\infty }$ performance of a single-area discrete-time power system with time-varying transmission delays under Denial-of-Service (DoS) attacks. First, the power system is modeled as a discrete-time delay system that integrates both DoS-induced delays and transmission delays, with PI controllers incorporated for Load Frequency Control (LFC). Using advanced summation inequality techniques, a Lyapunov–Krasovskii Functional (LKF) is constructed to capture comprehensive system state information, enabling the derivation of less conservative stability criteria. The proposed stability criterion based on linear matrix inequalities (LMI) ensures asymptotic stability and meets the $H_{\infty }$ performance index, while considering norm-bounded external load disturbances. Two convex optimization algorithms are designed to obtain optimal controller gains, either for a given $H_{\infty }$ index or by searching within a specified index range. Numerical examples and MATLAB simulations validate the effectiveness of the method. The results demonstrate that the maximum allowable delay upper bounds (MADUBs) estimated by the proposed criterion are larger than those obtained by existing methods, with an increase of at least 1 s. This indicates a reduction in conservatism. Simulation trajectories of frequency deviation ($\Delta f$) and area control error (ACE) confirm that the system remains stable under DoS attacks, with responses converging to zero after transient oscillations. Full article

No-reference image quality assessment (NR-IQA) models achieve high correlation with human mean opinion scores (MOS) on clean benchmarks, yet recent work shows they can be highly vulnerable to small adversarial perturbations that severely degrade ranking consistency, including in black-box settings. We introduce the Spectral Robustness Mixer (SRM), a lightweight neck inserted between an NR-IQA backbone and regression head, designed to reduce adversarial sensitivity without changing the dataset, label format, or target metric. SRM couples (i) deep-to-shallow cross-scale fusion via a Nyström low-rank attention surrogate, (ii) ridge-conditioned landmark kernels with ridge regularization, solved via numerically stable small-matrix factorization (SVD/LU) to improve conditioning, and (iii) variance-aware entropy-regularized fusion gates with a bounded gain cap to limit gradient amplification. We evaluate SRM on TID2013 and KonIQ-10k under a white-box ${\mathcal{l}}_{\infty }/{\mathcal{l}}_2$ attack ensemble that includes per-image regression objectives and a correlation-aware pairwise inversion objective (a ranking-inspired surrogate for correlation inversion), with expectation-over-transformation (EOT) and anti-gradient masking checks. At $ϵ=4/255$ (${\mathcal{l}}_{\infty }$), SRM improves worst-case robust Spearman’s rank-order correlation coefficient (SROCC; defined as the minimum over our fixed attack ensemble) by an absolute $0.06$–$0.08$ SROCC points (i.e., correlation-coefficient units, not percentage gain) across datasets/backbones, while keeping clean SROCC within $0.00$–$0.01$ of the baseline. We observe similar trends for Pearson linear correlation coefficient (PLCC). Full article

This paper presents a comprehensive study aimed at improving the efficiency of unmanned aerial vehicles (UAVs) through the enhancement of their aerodynamic and mechanical structures. The research is based on coupled computational fluid dynamics (CFD) and finite element analysis (FEA). The airflow around the UAV was modeled using the Navier–Stokes equations, while the structural behavior was described by the equations of linear elasticity. A UAV configuration with a wingspan of 1.8 m and a mass-optimized structure was investigated for flight speeds in the range of 10–35 m/s and angles of attack from −5° to +15°. The results of the aerodynamic optimization, including airfoil thickness variation and smoothing of the wing–fuselage junction, showed a reduction in the drag coefficient by 9–12% and an increase in the lift-to-drag ratio by up to 11% in the cruise regime. The structural optimization based on replacing aluminum with a carbon-fiber composite material led to a reduction in the structural mass by 13–16%, a reduction in the structural strength criterion value by 18–22%, as confirmed by the Tsai–Wu failure analysis, and a reduction in wing-tip deflection by 20–25% under 3 g and 5 g load cases, while satisfying strength and stiffness requirements. The obtained results demonstrate that the proposed integrated aerodynamic and structural optimization approach significantly improves the overall performance, efficiency, and operational reliability of UAV systems. Full article

Aiming at the challenge that traditional signature schemes struggle to simultaneously achieve efficiency, resistance to quantum attacks, and privacy protection, this paper proposes a lattice-based conditional privacy-preserving certificateless aggregate signature method (LCP-CAS). The scheme adopts an unordered aggregation algorithm to compress multiple signatures, in arbitrary order, into a single fixed-length aggregate signature, thereby achieving linear scalability in verification complexity. Its security is based on the hardness of the Ring Short Integer Solution (RSIS) problem, ensuring post-quantum resistance. By incorporating a conditional privacy-preserving mechanism, the scheme realizes device anonymity while supporting identity traceability, thus balancing privacy protection with regulatory requirements. Security analysis shows that the scheme meets the security requirements, including integrity, non-repudiation, conditional privacy preservation, and resistance to collusion attacks. Compared with existing related schemes, LCP-CAS achieves reduces aggregation and verification overhead while maintaining practicality in large-scale settings such as industrial IoT and device monitoring. Full article

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear $\mathcal{O}\left(N\right)$ complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution. Full article