A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles
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The number of Connected and Autonomous Vehicles (CAVs) is increasing rapidly in various smart transportation services and applications due to many benefits to society, people, and the environment. Several research surveys were conducted in the domain of CAVs. Such surveys primarily focus on various security threats and vulnerabilities in the domain of CAVs to classify different types of attacks, impacts of attacks, attacks features, cyber-risk, defense methodologies against attacks, and safety standards in CAVs. However, the importance of attacks 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 system from unexpected hazards on the road. There are some surveys with a limited discussion on Attacks Detection and Prevention Systems (ADPS), but such surveys provide only partial coverage of different types of ADPS for CAVs. Furthermore, there is a scope for discussing security, privacy, and efficiency challenges in ADPS that can give an overview of important security and performance attributes. This survey paper presents the significance of CAVs, potential challenges in CAVs, and an explanation of important security and privacy properties, attack scenarios, possible attacks in CAV, and performance evaluation parameters for ADPS. This survey paper extensively provides a discussion on the overview of different ADPS categories and state-of-the-art research works based on each ADPS category that gives the latest findings in this research domain. This survey also discusses crucial and open security research problems that are required to be focused on a secure deployment of CAVs in the market.
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