Energy-efficient edge based intrusion detection system using machine learning

Edge based computing’s primary goal is to enhance network performance by storing and processing data in proximity to the end user. However, the rapid development of edge computing often overlooks security risks in powerful applications and large-scale computer systems. Security and privacy are essential prerequisites for both edge computing and the ecosystems built around it. Additionally, intrusion detection systems (IDS) are used to detect and analyze network traffic, identify anomalies, trigger alarms, and automatically block the traffic according to the sets of rules design in IDS. This paper discusses the Edge based IDS designed specifically for edge computing with accuracy 99.77% to detect the attacks with 30% of malicious electric vehicle include in the simulation focusing on its relevant processes, features, and procedures.