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Detection of Anomalies in Electric Vehicle Charging Sessions

Authors:

Christoph Krauß,

Matthias HollickAuthors Info & Claims

ACSAC '23: Proceedings of the 39th Annual Computer Security Applications Conference

Pages 298 - 309

https://doi.org/10.1145/3627106.3627127

Published: 04 December 2023 Publication History

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Abstract

Electric Vehicle (EV) charging involves a complex system with cyber-physical components, backend systems, and communication protocols. A potential security incident in this system can open up cyber-physical threats and, for instance, lead to EV battery fires or power grid blackouts. In this paper, we propose a hybrid Intrusion Detection System (IDS) method consisting of regression-based charging session forecasting and anomaly detection. The method considers an EV’s detailed charging behavior throughout a session and we discuss and evaluate different design choices. For anomaly detection, we consider both classification- and novelty-based models as well as an ensemble method to combine both models. We perform evaluations based on real-world EV charging session data with simulated attacks. Our results show that regression-based forecasting provides a significant increase in detection performance for attacks affecting individual reports during a charging session. Additionally, the proposed ensemble method, which combines artificial neural network-based classification and local outlier factor-based novelty detection, can maintain a low false alarm rate while offering good detection performance w.r.t. known attacks as well as generalization to previously unseen attacks. We thus argue that the proposed solution can provide a positive contribution to EV charging security, resilience, and trustworthiness.

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Cited By

Kern DKrauß CHollick M(2024)Attack Analysis and Detection for the Combined Electric Vehicle Charging and Power Grid DomainsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3664512(1-12)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3664512
Hashmi MGioffrè DNagels SVan Hertem D(2024)Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets2024 IEEE International Conference on Power System Technology (PowerCon)10.1109/PowerCon60995.2024.10870536(1-5)Online publication date: 4-Nov-2024
https://doi.org/10.1109/PowerCon60995.2024.10870536
Dong HKotenko I(2024)Enhancing Security in EV Charging Systems: A Hybrid Detection and Mitigation Approach2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom62794.2024.00014(1-8)Online publication date: 9-Dec-2024
https://doi.org/10.1109/CloudCom62794.2024.00014

Index Terms

Detection of Anomalies in Electric Vehicle Charging Sessions
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems
  2. Systems security
    1. Denial-of-service attacks
    2. Distributed systems security

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cover image ACM Other conferences

ACSAC '23: Proceedings of the 39th Annual Computer Security Applications Conference

December 2023

836 pages

ISBN:9798400708862

DOI:10.1145/3627106

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2023

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Hessian Ministry of Higher Education, Research, Science and the Arts
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Federal Ministry of Education and Research

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ACSAC '23

ACSAC '23: Annual Computer Security Applications Conference

December 4 - 8, 2023

TX, Austin, USA

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Overall Acceptance Rate 104 of 497 submissions, 21%

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Cited By

Kern DKrauß CHollick M(2024)Attack Analysis and Detection for the Combined Electric Vehicle Charging and Power Grid DomainsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3664512(1-12)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3664512
Hashmi MGioffrè DNagels SVan Hertem D(2024)Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets2024 IEEE International Conference on Power System Technology (PowerCon)10.1109/PowerCon60995.2024.10870536(1-5)Online publication date: 4-Nov-2024
https://doi.org/10.1109/PowerCon60995.2024.10870536
Dong HKotenko I(2024)Enhancing Security in EV Charging Systems: A Hybrid Detection and Mitigation Approach2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom62794.2024.00014(1-8)Online publication date: 9-Dec-2024
https://doi.org/10.1109/CloudCom62794.2024.00014

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