Autonomous network services using machine learning-based cybersecurity

Abstract

The crucial topic of protecting Controller Area Network (CAN) bus systems in autonomous vehicles against cyberattacks is covered in this research. The risk and consequences of cyberattacks on CAN systems rise considerably as vehicles become increasingly automated and linked, creating safety and security hazards. In order to identify anomalies and categorize traffic into attack or conventional categories, the study looks at the weaknesses of CAN buses and recommends using machine learning techniques like Decision Trees, Clustering, and Deep Learning. To improve the detection of anomalies and cyber-attacks in CAN systems, the suggested methods combine data balance, feature selection, and ensemble learning with a voting-based strategy. Metrics like accuracy, precision, recall, F1-score, and confusion matrix can be used to assess the presented approaches. According to the study's findings, these suggested solutions provide a more reliable and efficient way to identify cyber-attacks and anomalies in CAN systems, boosting the development of cyber security for autonomous vehicles. While outlining the necessity of information security and the advantages of autonomous vehicles, it also suggests cutting-edge ways to strengthen their security. Overall, this article highlights the urgent need for improved security measures in autonomous cars since cyberattacks pose a serious threat to the functioning of these vehicles in a safe and secure manner. The study proposes a potential approach to enhancing the security of CAN bus systems in autonomous vehicles by suggesting cutting-edge approaches for identifying anomalies and cyber-attacks.