Cybersecurity in Robotic Autonomous Vehicles: Machine Learning Applications to Detect Cyber Attacks
Cybersecurity in Robotic Autonomous Vehicles introduces a novel Intrusion Detection System (IDS) specifically designed for AVs, which leverages data prioritization in CAN IDs to enhance threat detection and mitigation. It offers a pioneering intrusion detection model for AVs that uses machine and deep learning algorithms.
Presenting a new method for improving vehicle security, the book demonstrates how the IDS have incorporated machine learning and deep learning frameworks to analyze CAN Bus traffic and identify the presence of any malicious activities in real time with high level of accuracy. It provides a comprehensive examination of the cybersecurity risks faced by AVs with a particular emphasis on CAN vulnerabilities and the innovative use of data prioritization within CAN IDs.
The book will interest researchers and advanced undergraduate students taking courses in cybersecurity, automotive engineering, and data science. Automotive industry and robotics professionals focusing on internet-of-vehicles and cybersecurity will also benefit from the contents.
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