Securing Adaptive Autosar: Leveraging Virtual Patching for Enhanced Vehicle Network Security

Abstract

Vehicular communication technology promises to revolutionize transportation by enabling seamless interaction between vehicles and infrastructure, enhancing safety, efficiency, and convenience. However, this connectivity also exposes V2X systems to cybersecurity threats, including eavesdropping, denial of service (DoS) attacks, and man-in-the-middle (MitM) attacks, which can compromise communication integrity and jeopardize safety-critical operations. Traditional patching methods may be inadequate due to their limitations in agility and responsiveness to evolving threats. In this context, virtual patching emerges as a promising approach to swiftly mitigate known vulnerabilities and threats without requiring immediate physical updates. This paper discusses the virtual patching methodology for V2X networks, encompassing vulnerability assessment, threat intelligence gathering, virtual patch creation, dynamic deployment, monitoring, validation, response, and remediation. Furthermore, the integration of AI-driven intrusion detection systems enhances threat detection and response capabilities by leveraging machine learning algorithms to detect and analyze suspicious behaviors in real time. Implementing virtual patching and AI-based security mechanisms is essential to fortify V2X networks against cyber threats, ensuring their resilience and maximizing their transformative potential in modern transportation.