Deep Learning Based Intrusion Detection System for Network Security in IoT System - Joseph, Jennifer E; Aleke, Ngozi Tracy; Onyeanisi, Onyinyechukwu Prisca (Darul Yasin Al Sys, 2025)
The Internet of Things (IoT) has grown rapidly, leading to unparalleled connectivity and vast amounts of data. Anomaly detection plays a crucial role in identifying unusual behavior that deviates from the system's normal operation, enabling the swift detection and resolution of these anomalies. The integration of artificial intelligence (AI) with IoT significantly improves the effectiveness of anomaly detection, enhancing the performance, dependability, and security of IoT systems. AI-powered anomaly detection methods can recognize a wide array of threats within IoT environments, such as brute force attacks, buffer overflows, injection attacks, replay attacks, Distributed Denial of Service (DDoS) attacks, SQL injection, and backdoor threats. Intelligent Intrusion Detection Systems (IDS) are essential for IoT devices, as they help monitor networks for intrusions or anomalies. With the increasing adoption of IoT across various industries and its extensive attack surface, it offers more opportunities for malicious actors to exploit vulnerabilities. This paper reviews existing literature on anomaly detection in IoT systems using machine learning and deep learning approaches. It discusses the challenges associated with detecting intrusions and anomalies in IoT environments, emphasizing the rise in attacks. Recent advancements in machine learning and deep learning techniques for anomaly detection in IoT networks are examined, and the paper concludes that there is a need for further enhancement of these systems through the use of diverse datasets, real-time testing, and scalability improvements.