Identifying Intrusion Attempts on Connected and Autonomous Vehicles: A Survey
Emad E. Abdallah, Ahmad Aloqaily, Hiba Fayez
Abstract
Self-driving and connected cars are designed to make traffic safer while lowering risks and accidents. However, Security concerns about these vehicles limit their use and what they can accomplish. Cyber-attacks on these vehicles may have serious repercussions, including the loss of personal information as well as physical harm or even death. In this paper, we investigate intrusion detection on connected and autonomous vehicles using supervised machine learning techniques. The overarching goal is to develop a taxonomy for linked intrusion detection systems and supervised learning algorithms. To that end, we provide a thorough explanation of the concepts of intrusion detection systems and cyber-security attacks. Then, we demonstrate how machine learning can improve the security of future connected and autonomous vehicles by examining and evaluating how to protect sent and transferred data. Finally, a taxonomy based on these related works is offered. Based on a review of four well-known data sets in this field, we can conclude from this taxonomy that the classification performance of supervised learning algorithms is strong and encouraging.