Byzantine-Robust Federated Learning Framework with Post-Quantum Secure Aggregation for Real-Time Threat Intelligence Sharing in Critical IoT Infrastructure

The proliferation of Internet of Things devices in critical infrastructure has created unprecedented cybersecurity challenges, necessitating collaborative threat detection mechanisms that preserve data privacy while maintaining robustness against sophisticated attacks. Traditional federated learning approaches for IoT security suffer from two critical vulnerabilities: susceptibility to Byzantine attacks where malicious participants poison model updates, and inadequacy against future quantum computing threats that can compromise cryptographic aggregation protocols. This paper presents a novel Byzantine-robust federated learning framework integrated with post-quantum secure aggregation specifically designed for real-time threat intelligence sharing across critical IoT infrastructure. The proposed framework combines a adaptive weighted aggregation mechanism with lattice-based cryptographic protocols to simultaneously defend against model poisoning attacks and quantum adversaries. We introduce a reputation-based client selection algorithm that dynamically identifies and excludes Byzantine participants while maintaining differential privacy guarantees. The secure aggregation protocol employs CRYSTALS-Kyber for key encapsulation and homomorphic encryption to ensure confidentiality during parameter updates. Experimental evaluation on industrial IoT intrusion detection datasets demonstrates that our framework achieves 96.8% threat detection accuracy while successfully mitigating up to 40% Byzantine attackers, with only 18% computational overhead compared to non-secure federated approaches. The framework maintains sub-second aggregation latency suitable for real-time applications and provides 256-bit post-quantum security level.

Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks

As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major drawbacks: lack of interpretability and high computational cost. Black-box AI models make it difficult for security analysts to understand the reasoning behind their predictions, limiting their practical deployment. Moreover, conventional deep learning techniques demand significant computational resources, rendering them unsuitable for edge devices with limited processing power. To address these issues, this study introduces an Explainable and Lightweight AI (ELAI) framework designed for real-time cyber threat detection in edge networks. Our approach integrates interpretable machine learning algorithms with optimized lightweight deep learning techniques, ensuring both transparency and computational efficiency. The proposed system leverages decision trees, attention-based deep learning, and federated learning to enhance detection accuracy while maintaining explainability. We evaluate ELAI using benchmark cybersecurity datasets, such as CICIDS and UNSW-NB15, assessing its performance across diverse cyberattack scenarios. Experimental results demonstrate that the proposed framework achieves high detection rates with minimal false positives, all while significantly reducing computational demands compared to traditional deep learning methods. The key contributions of this work include: (1) a novel interpretable AI-based cybersecurity model tailored for edge computing environments, (2) an optimized lightweight deep learning approach for real-time cyber threat detection, and (3) a comprehensive analysis of explainability techniques in AI-driven cybersecurity applications.

Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions

Autonomous vehicles (AVs) are transforming modern transportation, but their reliability and safety are significantly challenged by harsh weather conditions such as heavy rain, fog, and snow. These environmental factors impair the performance of cameras, LiDAR, and radar, leading to reduced situational awareness and increased accident risks. Conventional cloud-based AI systems introduce communication delays, making them unsuitable for the rapid decision-making required in real-time autonomous navigation. This paper presents a novel Edge AI-driven real-time decision-making framework designed to enhance AV responsiveness under adverse weather conditions. The proposed approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for improved perception, alongside reinforcement learning (RL)-based strategies to optimize vehicle control in uncertain environments. By processing data at the network edge, this system significantly reduces decision latency while improving AV adaptability. The framework is evaluated using simulated driving scenarios in CARLA and real-world data from the Waymo Open Dataset, covering diverse weather conditions. Experimental results indicate that the proposed model achieves a 40% reduction in processing time and a 25% enhancement in perception accuracy compared to conventional cloud-based systems. These findings highlight the potential of Edge AI in improving AV autonomy, safety, and efficiency, paving the way for more reliable self-driving technology in challenging real-world environments.