## 2 Related Works
### Related Previous Studies  
Looking at related previous studies, [PERSON] et al. [44] performed systematic mapping on vehicle security measures but only targeted the network within the vehicle, not the autonomous vehicles as a whole. [PERSON] et al. [45] described the technological stages of autonomous vehicles and the operating principles, consisting of perception, judgment, and control, and explained the security threats that may occur in autonomous vehicles and the standardization of security technologies such as IEEE 1609.2 and ISO 14229. [PERSON] et al. [46] proposed a risk assessment process based on ISO 21434 to respond to accidental vehicle failures and cyber security threats, but they focused only on risk assessment as part of administrative security. [PERSON] et al. [47] described that denial of service, replay, and false data injection attacks could pose threats to intra-vehicle networks, inter-vehicle networks, and sensors. [PERSON] et al. [48] presented Sybil attacks, DoS, vehicular malware, and timing attacks on vehicular networks. [PERSON] et al. [49] described that the defense of the CAN bus, which plays a critical role in vehicles, is essential because the CAN protocol is vulnerable to security attacks such as frame injection and denial of service. [PERSON] et al. [50] presented that sensors, cameras, and vehicle communication mechanisms on the roadside or from other vehicles are easy targets for hacker attacks and suggest that a defense strategy is needed. [PERSON] et al. [51] categorized vulnerabilities and cybersecurity risks into attacks within vehicles, between vehicles, and vehicle infrastructure. They focus on network sectors such as lightweight security models, V2X security, and dynamic risks. [PERSON] et al. [52] presented a taxonomy useful for threat analysis and risk management from the perspective of autonomous vehicle security attacks. [PERSON] et al. [53] analyzed the security mechanism of the autonomous vehicle ecosystem and suggested security measures for the safe operation of the ecosystem. [PERSON] et al. [54] classified network monitoring techniques for security and compared general IT security measures with the ones in the vehicle security field. [PERSON] et al. [55] identified trends in autonomous vehicle technology and security vulnerabilities such as V2X remote access and flooding within the CAN bus. They defined vehicle system-attack scenarios targeting some functions and described countermeasures. They also presented security requirements such as message authentication or encryption for CAN protection. [PERSON] [56] described cyberattacks that may occur as autonomous vehicles become commercialized and mentioned 10 security threats and security requirements of the Smart Transportation Cyber Security Guide, including development and management protection, physical protection, authentication, passwords, and data protection. The author also presented safety improvement measures, such as building an integrated control system and security education. [PERSON] et al. [57] described vulnerabilities in the CAN bus, such as encryption and authentication. They also identified vulnerabilities in the CAN bus and the resulting vulnerabilities in Remote Control Execution and proposed solutions to counter them. [PERSON] et al. [58] proposed a dataset for developing a machine learning model that can detect DoS attacks in a V2V communication environment. The dataset was generated by reflecting the properties of the V2V communication environment and attack characteristicsusing simulation tools such as OMNeT++, SUMO, Veins, and INET. In addition, they described that the dataset could detect DoS attacks in various machine learning models.  
However, most related studies are fragmentary and focused on security technology trends, and there is a lack of research and efforts to analyze the items and check the levels of autonomous vehicle security and their relative priorities.