Enhancing Cyber Security in Autonomous Vehicles: A Hybrid XG Boost-Deep Learning Approach for Intrusion Detection in the CAN Bus

Abstract

As autonomous vehicles grow more common, maintaining their cyber security becomes increasingly important. The CAN (Controller Area Network) bus, a critical communication network in self-driving cars, is susceptible to cyber-attacks that can jeopardize vehicle safety and performance. In this paper, we offer a novel hybrid approach, DeepXG, that combines XGBoost and deep learning (DL) approaches to detect intrusions in the CAN bus. Our model takes advantage of both algorithms' strengths to extract critical characteristics and learn complicated patterns for accurate and resilient intrusion detection. We conducted comprehensive studies to evaluate DeepXG's performance using a genuine CAN traffic dataset from a CAV's OBD-2 port. The proposed method outperformed many intrusion detection methods, achieving an amazing accuracy of 99.90%. The XGBoost feature relevance score enables effective feature selection while reducing computing complexity and boosting generalization. Our findings show that DeepXG helps improve cyber security in autonomous vehicles. The hybrid model's ability to effectively detect and classify network intrusions makes it a potential approach for safeguarding the CAN bus and ensuring autonomous vehicle safety.