Oct 2026· Journal of Theoretical and Applied Information Technology
Network Security and Intrusion Detection
Abstract
As the Internet of Things (IoT) continues to grow exponentially, securing interconnected devices against evolving cyber threats has become paramount. This paper explores the development of an autonomous cyber defence system leveraging artificial intelligence (AI) to enhance intrusion detection for IoT networks. Traditional security measures fall short in resource-constrained, heterogeneous environments like IoT. Hence, we propose an AI-driven Intrusion Detection System (IDS) capable of real-time threat detection and autonomous response. Using a hybrid machine learning approach and reinforcement learning for automated mitigation, the system is evaluated on publicly available IoT security datasets. Results demonstrate improved detection accuracy, reduced false alarms, and efficient resource utilization. This research contributes to the advancement of intelligent, self-governing security mechanisms in IoT ecosystems.
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