Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

Abstract

IoT comprises sensors and other small devices interconnected locally and via the Internet. Typical IoT devices collect data from the environment through sensors, analyze it and act back on the physical world through actuators. We can find them integrated into home appliances, Healthcare, Control systems, and wearables. This chapter presents a variety of applications where IoT devices are used for anomaly detection and correction. We review recent advancements in Machine/Deep Learning Models and Techniques for Anomaly Detection in IoT networks. We describe significant in-depth applications in various domains, Anomaly Detection for IoT Time-Series Data, Cybersecurity, Healthcare, Smart city, and more. The number of connected devices is increasing daily; by 2025, there will be approximately 85 billion IoT devices, spreading everywhere in Manufacturing (40%), Medical (30%), Retail, and Security (20%). This significant shift toward the Internet of Things (IoT) has created opportunities for future IoT applications. The chapter examines the security issues of IoT standards, protocols, and practical operations and identifies the hazards associated with the existing IoT model. It analyzes new security protocols and solutions to moderate these challenges. This chapter’s outcome can benefit the research community by encapsulating the Information related to IoT and proposing innovative solutions.