A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles
Trupil Limbasiya, Ko Zheng Teng, Sudipta Chattopadhyay, and Jianying, Zhou

TL;DR
This paper provides a comprehensive survey of attack detection and prevention methods in connected and autonomous vehicles, highlighting gaps, challenges, and recent advancements in securing CAV systems against cyber threats.
Contribution
It offers an extensive analysis of ADPS categories for CAVs, addressing security, privacy, and performance challenges, and identifies open research problems in the domain.
Findings
Analysis of various ADPS categories for CAVs
Identification of security and privacy challenges in CAVs
Discussion of open research problems in attack detection and prevention
Abstract
The number of Connected and Autonomous Vehicles (CAVs) is increasing rapidly in various smart transportation services and applications, considering many benefits to society, people, and the environment. Several research surveys for CAVs were conducted by primarily focusing on various security threats and vulnerabilities in the domain of CAVs to classify different types of attacks, impacts of attacks, attack features, cyber-risk, defense methodologies against attacks, and safety standards. However, the importance of attack detection and prevention approaches for CAVs has not been discussed extensively in the state-of-the-art surveys, and there is a clear gap in the existing literature on such methodologies to detect new and conventional threats and protect the CAV systems from unexpected hazards on the road. Some surveys have a limited discussion on Attacks Detection and Prevention…
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Taxonomy
TopicsVehicular Ad Hoc Networks (VANETs) · Blockchain Technology Applications and Security · Autonomous Vehicle Technology and Safety
