Machine Learning based Systems for Intrusion Detection in VANETs

It is critical to identify attacks in Vehicle Ad-hoc Networks (VANETs) in order to provide more dependable and safe communication amongst all of the system’s cars. One of the most important parts of the Internet-connected VANETs is the Intrusion Detection System (IDS), which requires dependable and secure communication between all of the system’s vehicles. In this study, vehicle network attacks that significantly compromise system availability, such as spoofing, flooding, denial-of-service (DoS), replaying attacks, benign packets, and evil twins are anticipated and identified using a VANET IDS. Influencing artificial intelligence is a superb method for developing network intrusion detection (Deep Learning). Influencing artificial intelligence is a superb method for developing network intrusion detection (Deep Learning). This is due to the algorithms’ ability to pick up on complex VANET activities, which they then apply to recognize and block threats to the vehicle network. In order to recognize intrusions and attacks in car networks, we have used a DNN deep method in this study to develop an auto encoder. Regarding the attack types in the dataset that involve flooding, impersonation, and injection, the suggested VIDS demonstrates an accuracy of 99%, yielding remarkably good results.