Cybersecurity in Robotic Autonomous Vehicles

Machine Learning Applications to Detect Cyber Attacks

Ahmed Alruwaili, Sardar M. N. Islam, Iqbal Gondal

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040358702
• Edition: 0
• Topic: Computer Science
• Subtopic: Cyber Security
• Index: Computer Science

Cybersecurity in Robotic Autonomous Vehicles introduces a novel intrusion detection system (IDS) specifically designed for AVs, which leverages data prioritisation 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 has incorporated machine learning and deep learning frameworks to analyse 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 prioritisation 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.

Table of contents

• Cover
• Half Title
• Title
• Copyright
• Contents
• Preface
• About the Authors
• List of Figures
• List of Tables
• List of Abbreviations
• 1 Introduction
• 2 Theoretical Lens
• 3 Exploring CAN Bus Security: Insights and Analysis
• 4 Research Design
• 5 Results and Discussion
• 6 Conclusions and Future Research
• References
• Appendix
• Index