CYBERSECURITY IN AUTOMOTIVE NETWORKS: MITIGATING THREATS IN CAN, LIN AND AUTOMOTIVE ETHERNET SYSTEMS

The increasing complexity and interconnectivity of modern vehicles have introduced significant cybersecurity challenges in in-vehicle communication networks, particularly in Controller Area Network (CAN), Local Interconnect Network (LIN), and Automotive Ethernet. While these protocols facilitate seamless data exchange between Electronic Control Units (ECUs), they were originally designed without robust security features, making them vulnerable to message spoofing, denial-of-service (DoS) attacks, and unauthorized access. Existing mitigation strategies, including message authentication, hardware security modules (HSMs), and intrusion detection systems (IDSs), have provided partial solutions but still face latency, computational overhead, and backward compatibility challenges.This study explores alternative security approaches for enhancing CAN bus security, including lightweight cryptographic algorithms, real-time software-based monitoring, and AI-driven anomaly detection. Additionally, emerging technologies such as blockchain-based authentication and machine learning-based intrusion detection offer promising avenues for securing in-vehicle networks against evolving cyber threats. The research also highlights the need for standardized security regulations to ensure industry-wide adoption of robust cybersecurity frameworks. By integrating cryptographic security measures, AI-powered detection mechanisms, and regulatory compliance, automakers can significantly enhance the resilience of vehicle communication networks, ensuring safe and secure mobility in the era of connected and autonomous vehicles.