A Comprehensive Analysis of the Security of ML Integrated CPS as Autonomous Vehicles

Abstract

The rapid advancement in the deployment of Cyber-Physical Systems (CPS) within autonomous vehicles has taken numerous of security challenges. These challenges primarily arise from the intricate and interconnected nature of CPS components, which can lead to vulnerabilities during operation. The complexity of these systems often results in connected devices undergoing errors, and there is an occurrence of risk at various cyber-attacks. In addition to security threats reliable and concerns the impact of overall CPS functionality and safety. Besides, the security and reliability problems have stimulates in protection and detection methodologies. A significant instance of improvement in this field is the incorporation of Machine Learning (ML) techniques into CPS, which has effectively streamlined operations and reduced the complexity associated with autonomous vehicles. This paper aims to provide a comprehensive review of the security measures pertinent to autonomous vehicles, specifically focusing on the integration of ML techniques within CPS. It delves into several protection approaches considered to address the multiple reliability along with security challenges handled by vehicles. Furthermore, the paper surveys the crucial feature of risk assessment associated to autonomous vehicles, emphasising the performance can considerably improve when risk assessments are confirmed by large datasets in the training phase, predominantly while engaging learning models. In general, the findings emphasise the significance of leveraging progressive data analytics and ML to improve the security and reliability of autonomous vehicles, confirming the safe functions in progressively multifaceted atmospheres.