학술논문

Evaluation Framework for Electric Vehicle Security Risk Assessment
Document Type
Periodical
Source
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 25(1):33-56 Jan, 2024
Subject
Transportation
Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Security
Risk management
Privacy
Electric vehicles
Autonomous vehicles
Vehicle-to-everything
Transportation
Security profiling
risk assessment
electric vehicle
autonomous vehicle
cybersecurity attacks
Language
ISSN
1524-9050
1558-0016
Abstract
Electric Vehicles (EVs) seem promising for future transportation to solve environmental concerns and energy management problems. According to Reuters, global car makers plan to invest over half a billion in more efficient and intelligent EVs and batteries. However, there are several challenges in EV mass production, including cybersecurity. Due to the cyber-physical nature of EVs and charging stations, their security and trustworthiness are ongoing challenges. In this study, we identify gaps in the security profiling of EVs and categorize them into five components: 1) charging station security, 2) information privacy, 3) software security, 4) connected vehicle security, and 5) autonomous driving security. Our study provides a comprehensive analysis of identified vulnerabilities, threats, challenges and attacks for different EV security aspects, along with their possible surface/subsurface and countermeasures. We develop a comprehensive security risk assessment framework by first using EV security profiles and mapping identified vulnerabilities to a well-known threat model, STRIDE. Then, we classify the risk levels associated with each vulnerability by setting ground criteria for the impact and likelihood of the threats. Finally, we validate our risk assessment framework by applying the same criteria to eight real-world EV attack scenarios. As a result, researchers can adapt the proposed risk assessment framework to discover threats and assess their risks in EVs and charging station ecosystems.