Machine Learning-Based Detection and Prevention Systems for IoE

Internet of Everything (IoE) has gained popularity due to its services and application to enhance the quality of living standards. Although these networks are providing smart, cost-effective, integrated services to users and play a significant role in the world economy, security is one of the serious concerns, especially from denial of services (DoS) and distributed denial of services (DDoS) attacks. These attacks are serious challenges, especially for limited resource IoE devices. Machine learning (ML) approaches have been adopted for attack detection, especially in Intrusion Detection Systems (IDS). These systems have suffered from high network overhead and latency issues which lead to slow detection and unresponsiveness. This chapter presents the machine and deep learning-based detection and prevention methods for IoE networks along with their advantages and disadvantages to protecting the networks from unknown attacks.