An Intelligent Data-Driven Model to Secure Intravehicle Communications Based on Machine Learning

This paper presents a compact yet potent approach to fortify intra-vehicle communication security through machine learning.Our model combines anomaly detection, threat classification, and encryption to efficiently identify and counter potential cyber threats.Initially, anomaly detection algorithms scrutinize network traffic, swiftly pinpointing suspicious patterns indicative of intrusion attempts or unauthorized access.This continuously learning system adapts to emerging threats, enhancing accuracy over time.Upon anomaly detection, classification algorithms categorize threats, facilitating prioritized responses and optimal resource allocation.Lastly, encryption techniques safeguard data transmission within the vehicle network, ensuring confidentiality and integrity against eavesdropping or tampering.This autonomous model requires minimal human intervention, providing robust protection for connected and autonomous vehicles.Experimental validation showcases its effectiveness, contributing to the security of modern automotive systems.