Abstract

Green Internet of Things (IoT) encompasses various sectors like farming, home automation, smart transport, and wellness, aiming to simplify people's lives. However, cyberattacks pose a significant threat to IoT devices. Deep learning techniques have emerged as effective solutions for IoT security, particularly in intrusion detection. Anomaly-based Intrusion Detection Systems (IDS) are favored over signature-based detection to combat zeroday threats. This study provides a comprehensive literature review on deep learning-based anomaly detection and device classification for safeguarding IoT systems.