An Investigation of Cyber-attacks and Security Detection for route diversion Scenario in Connected Autonomous Vehicles

Abstract

The original intent of developing autonomous vehicles was to alleviate a number of problems, such as occupational hazards, traffic jams, pollution, and hazardous gas emissions. A network of interconnected sensors, actuators, and devices—including lidar, global positioning systems, radar, onboard computers, cameras, and more—allows connected vehicles to learn how to operate in real-world conditions. However, data from recent cyberattacks, cybercrimes, and mishaps suggests that malicious organizations can access this sensor data or even the main control system through hacking, leading to a variety of losses. Over the last several years, we have noticed that the government and automakers have been avoiding spending money on fully automated cars. As time goes by, fewer and fewer individuals are interested in AVs, and numerous surveys point to the absence of a robust security architecture as the main reason. An overview of cyberattacks on AVs and methods to counter them are presented in this study. Last but not least, research on cyber-attacks and security detection in CAVs reveals the importance of robust cybersecurity frameworks in protecting these systems against route diversion scenarios. Cyber vulnerabilities, especially those involving route manipulation, pose a danger to the security and dependability of autonomous driving as more and more CAVs are integrated into modern transportation networks. This study highlights the importance of better detection systems to swiftly identify and eliminate cyber risks, as well as manage attempts to divert routes in order to avoid accidents, traffic jams, and security breaches. Safe, secure, and robust CAV networks are feasible in smart mobility, which will help build public confidence in autonomous technology. However, this can only be achieved with appropriate cybersecurity measures.