arXiv subjects

Omar Abdul Wahab

Publications and source records attributed to Omar Abdul Wahab.

4 recordsLinked to original sources

arXiv2026

Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures

Time integrity across distributed Internet of Things (IoT) devices is fundamental to reliable sensing, control, and security in energy cyber-physical systems. However, operational energy IoT systems remain vulnerable to clock-drift escalation, time-synchronization manipulation, and catastrophic timestamp discontinuities, e.g., the Year 2038 (Y2K38) Unix epoch overflow. These failures violate timestamp monotonicity, distort temporal ordering, and introduce structured inconsistencies in system observations. Conventional anomaly detection models, which typically assume reliable and uniformly ordered timestamps, are therefore ill-equipped to capture timing-layer failures. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a clock-dynamics-aware anomaly detection solution that jointly models temporal distortion and inter-device consistency in energy IoT systems. STGAT integrates drift-aware temporal embeddings and temporal self-attention to capture non-uniform and corrupted time evolution within individual device streams, while graph attention models the spatial propagation of timing inconsistencies across interconnected nodes. A Jacobian-regularized latent representation further promotes geometric separation between nominal clock evolution and anomalous temporal deformation caused by drift escalation, synchronization offsets, jitter accumulation, and epoch-overflow events. Experimental evaluation on energy IoT telemetry augmented with controlled timing-layer perturbations shows that STGAT achieves 95.7% accuracy, 94.0% precision, 92.0% recall, 93.0% F1-score, and 0.97 AUC under the primary test setting. STGAT also reduces detection delay to 2.3 time steps, corresponding to a 26% improvement over the closest baseline, while maintaining stable performance under overflow-induced discontinuities, stealthy drift escalation, and temporally induced physical inconsistencies.

cs.LG↗

arXiv2026

EcoDefender: Energy-Efficient Hybrid Anomaly Detection for IoT Edge Gateways

The rapid growth of the Internet of Things (IoT) has created large-scale, heterogeneous ecosystems that are increasingly vulnerable to sophisticated, distributed cyber threats. However, many existing anomaly detection systems prioritize detection accuracy while overlooking system-level constraints, such as latency, computational overhead, and energy consumption, thereby limiting their practicality for resource-constrained edge gateways. This paper presents EcoDefender, an edge-oriented hybrid anomaly detection framework that combines Autoencoder (AE)-based latent representation learning with Isolation Forest (IF) anomaly scoring for IoT traffic analysis. The proposed architecture introduces several enhancements over conventional AE-IF pipelines, including anomaly-aware latent manifold regularization, variance-weighted isolation splits in the latent space, and a learnable fusion mechanism that adaptively combines reconstruction error and isolation-based anomaly scores in the presence of potential distributional drift. By compressing high-dimensional traffic features into compact latent representations and performing anomaly scoring in this reduced space, EcoDefender enables lightweight and fully unsupervised anomaly detection suitable for edge deployment. An experimental evaluation of realistic IoT traffic and a distributed Raspberry Pi edge testbed demonstrates that EcoDefender achieves up to 94% detection accuracy while maintaining low computational overhead, with an average CPU usage of 22% and an end-to-end inference latency of 27 ms. Furthermore, energy-aware measurements obtained through device-level power monitoring show an average energy consumption of 0.45 J per inference (0.28 g CO2 emissions), representing a 30% reduction in energy consumption compared with AE-only baselines while sustaining inference throughput of up to 5,000 samples per second.

cs.CR↗

arXiv2026

Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation

Federated learning (FL) has become an effective paradigm for privacy-preserving, distributed Intrusion Detection Systems (IDS) in cyber-physical and Internet of Things (IoT) networks, where centralized data aggregation is often infeasible due to privacy and bandwidth constraints. Despite its advantages, most existing FL-based IDS assume closed-set learning and lack mechanisms such as uncertainty estimation, semantic generalization, and explicit modeling of epistemic ambiguity in zero-day attack scenarios. Additionally, robustness to heterogeneous and unreliable clients remains a challenge in practical applications. This paper introduces a semantics-driven federated IDS framework that incorporates language-derived semantic supervision into federated optimization, enabling open-set and zero-shot intrusion detection for previously unseen attack behaviors. The approach constructs semantic attack prototypes using a Tri-LLM ensemble of GPT-4o, DeepSeek-V3, and LLaMA-3-8B, aligning distributed telemetry features with high-level attack concepts. Inter-LLM semantic disagreement is modeled as epistemic uncertainty for zero-day risk estimation, while a trust-aware aggregation mechanism dynamically weights client updates based on reliability. Experimental results show stable semantic alignment across heterogeneous clients and consistent convergence. The framework achieves over 80% zero-shot detection accuracy on unseen attack patterns, improving zero-day discrimination by more than 10% compared to similarity-based baselines, while maintaining low aggregation instability in the presence of unreliable or compromised clients.

cs.CR↗

arXiv2024

TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on Pre-trained Language Models

With the widespread of digital environments, reliable authentication and continuous access control has become crucial. It can minimize cyber attacks and prevent frauds, specially those associated with identity theft. A particular interest lies on keystroke dynamics (KD), which refers to the task of recognizing individuals' identity based on their unique typing style. In this work, we propose the use of pre-trained language models (PLMs) to recognize such patterns. Although PLMs have shown high performance on multiple NLP benchmarks, the use of these models on specific tasks requires customization. BERT and RoBERTa, for instance, rely on subword tokenization, and they cannot be directly applied to KD, which requires temporal-character information to recognize users. Recent character-aware PLMs are able to process both subwords and character-level information and can be an alternative solution. Notwithstanding, they are still not suitable to be directly fine-tuned for KD as they are not optimized to account for user's temporal typing information (e.g., hold time and flight time). To overcome this limitation, we propose TempCharBERT, an architecture that incorporates temporal-character information in the embedding layer of CharBERT. This allows modeling keystroke dynamics for the purpose of user identification and authentication. Our results show a significant improvement with this customization. We also showed the feasibility of training TempCharBERT on a federated learning settings in order to foster data privacy.

cs.CR↗