IDSSA: An Intrusion Detection System with Self-adaptive Capabilities for Strengthening the IoT Network Security

This paper introduces a novel intrusion detection system (IDS) with self-adaptive capabilities, specifically designed to enhance the security of Internet of Things (IoT) networks. The rapid growth and interconnectivity of IoT devices expose them to a wide array of cyber threats, demanding intelligent and adaptable defense mechanisms. Our proposed IDS utilizes advanced machine learning algorithms, “Dynamic Neural Sentry (DNS)” and “Evolutionary Anomaly Tracker (EAT)” for real-time intrusion detection. DNS employs deep neural networks with recurrent feedback to learn from new attack patterns and adapt its detection strategies rapidly, enhancing accuracy in processing complex IoT network traffic. EAT uses genetic programming to evolve detection rules, identifying novel and sophisticated attacks beyond traditional signature-based systems. With an “IoT Contextual Analyzer” to reduce false positives, the self-adaptive IDS improves against emerging threats autonomously, outperforming static systems in detecting known and zero-day attacks, securing IoT networks efficiently.