TL;DR
This thesis presents an efficient and adversarial-resilient IDS framework to detect existing and previously unseen cyberattacks for SDN-based IoT networks.
This study proposes an AI driven vulnerability management framework that integrates anomaly detection techniques using machine learning to enhance proactive threat identification and mitigation in IoT ecosystems, and demonstrates that AI based models significantly outperform traditional methods in detecting previously...
An AI-driven framework for intrusion detection and energy-efficient routing in IoT networks is proposed that uses machine-learning techniques to identify malicious network traffic and classify it as normal or abnormal and applies an energy-aware routing strategy to select communication paths.
This paper aims to provide a foundational resource to guide future research on reliable, explainable, and practical IoT intrusion detection systems by identifying the problems addressed in current research and highlighting the limitations in the literature.
LightGBM demonstrates a slight edge in overall detection performance compared to other models, which highlights its effectiveness in detecting diverse and complex attack patterns across multiple traffic directions.
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