학술논문

Hidden Markov Models-Based Anomaly Correlations for the Cyber-Physical Security of EV Charging Stations
Document Type
Periodical
Source
IEEE Transactions on Smart Grid IEEE Trans. Smart Grid Smart Grid, IEEE Transactions on. 13(5):3903-3914 Sep, 2022
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Hidden Markov models
Computer security
Electric vehicle charging
Cyberattack
Reliability
Power grids
Protocols
Electric vehicle charging station
cyber-physical security
hidden Markov models
cybersecurity of EV charger
Language
ISSN
1949-3053
1949-3061
Abstract
Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network.