AI Innovations and Cybersecurity Challenges in Autonomous Vehicles

Abstract

Autonomous vehicles (AVs) are transforming transportation by improving road safety through advanced technologies. Technologies such as deep learning, computer vision, and reinforcement learning enable AVs to perceive their surroundings, navigate complex environments, and make critical decisions in real-time. However, their reliance on interconnected systems introduces significant cybersecurity challenges, exposing them to threats that could compromise safety and functionality. Addressing these risks requires robust solutions, including encryption, secure communication protocols, and real-time intrusion detection systems.

This thesis analyzes how artificial intelligence developments in autonomous vehicles evolve while analyzing the increasing cybersecurity threats these vehicles encounter. The examination analyzed the use of AI for enhancing intrusion detection by evaluating the Car Hacking Dataset utilizing machine learning models. In addition, this thesis assessed the present autonomous vehicle security practices to determine their compatibility with the NIST Cybersecurity Framework. Research shows that AI intrusion detection systems deployed with NIST-derived security standards significantly advance the security of AV networks and strengthen their capability to defend against changing threats.