AVTAR Cyber Shield: A Comprehensive Framework for Machine Learning-Enhanced Cybersecurity in Cyber-Physical Environment

abstract-In the rapidly advancing era of self-driving cars, the imperative of ensuring robust cybersecurity measures to safeguard against evolving threats becomes paramount. This paper investigates the intricate cyber-physical systems within autonomous vehicles, emphasizing the critical role of “AVTAR Cyber Shield” as a comprehensive cybersecurity framework for fortifying against potential vulnerabilities. The paper provides a basic understanding on how autonomous car manufacturers and/or technology providers like Pony.ai, Waymo, WeRide and Zoox implement safety measures which prompted us to introduce a new driver’s self-preparation safety module before riding to qualitatively evaluate the preride risk(s) associated with self-driving cars. An exploration of current cybersecurity threats, and compliance with industry standards, is complemented by the identification of challenges such as technical complexities and human factors. The paper concludes with analyzing the existing statistical result focus on recent risk calculation, contributing to the future trends and innovations, proposing recommendations that advocate for continuous monitoring, best practices, and adaptive strategies to address evolving cybersecurity threats and traditional human un/known mistakes. This comprehensive examination contributes valuable insights to the discourse on securing the future of self-driving vehicles, offering guidance for researchers, practitioners, and policymakers navigating the complex landscape of autonomous transportation.