AI-Sentinel: A Novel AI-Powered Intrusion Detection Approach Against Cyber Threats for In-Vehicular Communication Systems

The emergence of revolutionizing technologies such as Artificial Intelligence and the Internet of Things, and their integration intothe automotive industry has brought innovations and made the lives of common people easier and more complacent. Leveraging the advanced intelligent services provided byconnected and autonomous vehicles,the driving experience is much more convenient and effortless. The CAN (Controller Area Network) protocol is the mostdeployed protocol in in-vehicular communications in the ICVs (intelligent connected vehicles) environment due to its efficiency and speed. However, it lacks basic security mechanisms like encryption and authentication,making it vulnerable to various cyber threats. In this article, we have presented a novel, robust, cutting-edge AI-based Intrusion detection system for detecting various seen and unseen cyber-attacks in in-vehicular networks to ensure security. Two main models deployed in the proposed framework are RNN for dealing with temporal dependencies in the CAN traffic and LightGBM for efficient feature extraction. The experimental results show that the hybrid of these two models performs better in terms of various evaluation metrics, with its accuracy being 94% in classifying the CAN traffic into normal and different attack classes. A comparison with the existing state-of-the-art approaches shows that our proposed approach is more robust and secure, with it being deployed in a Federated Learning FL environment.