AI-Enhanced Intrusion Detection System for IoT Edge Networks

Abstract

The Internet of Things (IoT) connects billions of smart devices, creating a highly dynamic environment that demands efficient and secure data communication. However, IoT edge networks are highly vulnerable to cyberattacks due to limited resources and diverse communication protocols. To address these challenges, this paper presents an AI-Enhanced Intrusion Detection System (IDS) for IoT edge networks that integrates artificial intelligence with edge computing to achieve real-time threat detection and response. The proposed system employs machine learning and deep learning algorithms to analyze network traffic, detect anomalies, and accurately predict potential intrusions while minimizing false positives. By processing data at the edge, the system ensures low latency, scalability, and energy efficiency, overcoming the limitations of traditional cloud-based IDS solutions. Experimental evaluations demonstrate that the AI-based IDS improves detection accuracy, adapts to evolving attack patterns, and enhances the overall security, reliability, and resilience of IoT infrastructures. This study emphasizes the transformative potential of AI in developing intelligent, adaptive, and future-ready cybersecurity frameworks for next-generation IoT ecosystems.