Strategic Approaches to Intrusion Detection in Cloud-IoT Ecosystem

9Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems

J. Ramkumar^1* and S. Vetrivel²

¹Department of Computer Science, Kristu Jayanti University, Bengaluru, Karnataka, India

²Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, Coimbatore, Tamilnadu, India

Abstract

The chapter titled “Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems” delves into the critical role that deep learning techniques play in safeguarding Internet of Things (IoT) devices from adversarial attacks. With the proliferation of IoT applications across various domains, understanding and countering adversarial threats is paramount for maintaining the security and reliability of these systems. This chapter provides a comprehensive exploration of the methodologies and strategies employed to defend IoT systems against such attacks using deep learning. The primary aim of this chapter is to provide a detailed analysis of adversarial attacks targeting IoT systems and demonstrate how deep learning techniques can be effectively utilized to detect, mitigate, and prevent these threats. The chapter seeks to bridge the gap between theoretical advancements in deep learning and their practical applications in IoT security, offering readers both foundational knowledge and actionable insights. The chapter begins with an examination of the nature of adversarial attacks, detailing various techniques used by attackers and ...