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Cited by 76Smart Grid Security and ResilienceInformation and Cyber SecurityNetwork Security and Intrusion Detection

Hidden markov models-based anomaly correlations for the cyber-physical security of ev charging stations

Girdhar, Mansi, Hong, Junho, Lee, Hyojong, Song, Tai-Jin·2022·IEEE Transactions on Smart Grid
DOI 10.1109/tsg.2021.3122106
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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.

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Crossref · 10.1109/tsg.2021.3122106

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33 of 33 references verified0 citation errors · 0 not checkable

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  • Girdhar et al. [128]'s HMM-based framework simultaneously analyzes cyber events and physical responses, implementing a cross-domain state correlation structure that predicts process anomalies and attack phases.

    “Their HMM-based framework simultaneously analyzes cyber events and physical responses, implementing a cross-domain state correlation structure that predicts process anomalies and attack phases.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [128]

  • Girdhar et al. [128] used a simulated EV charging infrastructure dataset, defined key attack paths via STRIDE threat modeling and Attack-Defense Trees (ADT), and jointly observed cyber-layer communication events and physical-layer current, voltage, and temperature measurements on a unified time axis to learn state transition probabilities.

    “Using a simulated EV charging infrastructure dataset, they define key attack paths via STRIDE threat modeling and Attack-Defense Trees (ADT), and jointly observe cyber-layer communication events (e.g., DoS, MITM, data manipulation) and physical-layer current, voltage, and temperature measurements on a unified time axis to learn state transition probabilities.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [128]

  • Girdhar et al. [128] proposed a hybrid data-fusion anomaly detection method for electric vehicle (EV) fast-charging stations (EV XFC) that models cross-domain correlations across attack stages.

    “Girdhar et al. [128] proposed a hybrid data-fusion anomaly detection method for electric vehicle (EV) fast-charging stations (EV XFC) that models cross-domain correlations across attack stages.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [128]

  • Although Girdhar et al. [128] does not perform explicit feature-level fusion, the simultaneous tracking of cyber and physical states allows probabilistic modeling of multi-stage attacks and real-time estimation of their impact on physical system behavior.

    “Although it does not perform explicit feature-level fusion, the simultaneous tracking of cyber and physical states allows probabilistic modeling of multi-stage attacks-such as MITM, DoS, and FDI-and real-time estimation of their impact on physical system behavior.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [128]

  • Some hybrid data fusion works couple fused anomaly scores with higher-level decision modules-such as attack-stage modeling, ATT&CK-based threat mapping, or reinforcement learning-based model selection-so that hybrid data directly drives mitigation policies and adaptive defense strategies in real time.

    “Third, some works couple fused anomaly scores with higher-level decision modules-such as attack-stage modeling, ATT&CK-based threat mapping, or reinforcement learning-based model selection-so that hybrid data directly drives mitigation policies and adaptive defense strategies in real time [128,131,132].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [128]

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Related

Authors
Girdhar, M., Hong, J., Lee, H., & Song, T.-J.
Affiliations
  • University of Michigan (US)
  • US Hitachi Energy Research Center, Hitachi Energy, Raleigh, NC, USA
  • Chungbuk National University (KR)
ORCID
Venue
IEEE Transactions on Smart Grid
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-09
Volume
13
Issue
5
Pages
3903-3914
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
DOI
10.1109/tsg.2021.3122106
OpenAlex
W4205463438
Cited by
76
References
33
Citations by year
2026: 13 · 2025: 29 · 2024: 15 · 2023: 15 · 2022: 4
FWCI
3.78
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Smart Grid (publishedVersion)
Funders
Chungbuk National University Brain Korea (BK) 21 FOUR
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
electric vehicle charging stations, cyber-physical security, hidden Markov models, anomaly correlation, extreme fast charging, STRIDE threat modeling, attack scenarios, bidirectional charging, attack mitigation

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