Enhancing Cybersecurity in AI-Driven Autonomous Vehicle Systems through Behavioral Anomaly Detection

Abstract

This research paper explores the integration of behavioral anomaly detection techniques to enhance the cybersecurity of AI-powered autonomous vehicles. As the adoption of autonomous vehicles increases, so does the risk of cyber attacks that can compromise safety and functionality. This study proposes a framework that leverages machine learning algorithms to identify and respond to anomalous behaviors in real-time, thereby improving the resilience of autonomous systems against potential threats. The paper discusses the technical challenges, ethical considerations, and future implications of implementing such a framework, underscoring the necessity for proactive cybersecurity measures in the rapidly evolving automotive landscape.