Securing Intelligent Edge Ecosystems

Abstract

With the advent of Internet of Things technologies and intelligent edge computing systems, the surface of cyber-attacks has been increasingly growing, which results in the problem of network security in distributed systems. This chapter will provide a review of intelligent intrusion detection methods using AI for secure intelligent edge ecosystems in the IoT. The chapter will discuss how machine learning, deep learning, and federated learning models are used together with edge computing systems to increase detection accuracy, reduce the time needed for detection, and maintain scalability while preserving data privacy. Furthermore, the proposed framework takes into account explainable artificial intelligence, privacy-preserving learning, and deployment techniques to overcome problems related to limited resource constraints of edge devices. Also, this chapter explores present security issues, application scenarios, implementation issues, and future perspectives, such as zero trust architecture, collaborative edge intelligence, and autonomous cyber defense systems.