Skip to main content
SpringerLink
Log in

Collaborative Anomaly Detection System for Charging Stations

  • Conference paper
  • First Online:
Computer Security – ESORICS 2022 (ESORICS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13555))

Included in the following conference series:

Abstract

In recent years, the deployment of charging infrastructures has been increasing exponentially due to the high energy demand of electric vehicles, forming complex charging networks. These networks pave the way for the emergence of new unknown threats in both the energy and transportation sectors. Economic damages and energy theft are the most frequent risks in these environments. Thus, this paper aims to present a solution capable of accurately detecting unforeseen events and possible fraud threats that arise during charging sessions at charging stations through the current capabilities of the Machine Learning (ML) algorithms. However, these algorithms have the drawback of not fitting well in large networks and generating a high number of false positives and negatives, mainly due to the mismatch with the distribution of data over time. For that reason, a Collaborative Anomaly Detection System for Charging Stations (here referred to as CADS4CS) is proposed as an optimization measure. CADS4CS has a central analysis unit that coordinates a group of independent anomaly detection systems to provide greater accuracy using a voting algorithm. In addition, CADS4CS has the feature of continuously retraining ML models in a collaborative manner to ensure that they are adjusted to the distribution of the data. To validate the approach, different use cases and practical studies are addressed to demonstrate the effectiveness and efficiency of the solution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdar, M., Yen, N.Y., Hung, J.C.S.: Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision trees. J. Med. Biol. Eng. 38(6), 953–965 (2018)

    Article  Google Scholar 

  2. Alcaraz, C., Cazorla, L., Fernandez, G.: Context-awareness using anomaly-based detectors for smart grid domains. In: Lopez, J., Ray, I., Crispo, B. (eds.) CRiSIS 2014. LNCS, vol. 8924, pp. 17–34. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17127-2_2

    Chapter  Google Scholar 

  3. Alcaraz, C., Lopez, J., Wolthusen, S.: OCPP protocol: security threats and challenges. IEEE Trans. Smart Grid 8(5), 2452–2459 (2017)

    Article  Google Scholar 

  4. Open Charge Alliance: OCPP 2.0.1 (2020). https://www.openchargealliance.org/protocols/ocpp-201/. Accessed 24 May 2022

  5. Antoun, J., Kabir, M.E., Moussa, B., Atallah, R., Assi, C.: A detailed security assessment of the EV charging ecosystem. IEEE Netw. 34(3), 200–207 (2020)

    Article  Google Scholar 

  6. Basnet, M., Ali, M.H.: Deep learning-based intrusion detection system for electric vehicle charging station. In: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES, pp. 408–413 (2020)

    Google Scholar 

  7. Bhusal, N., Gautam, M., Benidris, M.: Cybersecurity of electric vehicle smart charging management systems. In: 52nd North American Power Symposium, NAPS (2020)

    Google Scholar 

  8. Brighente, A., Conti, M., Donadel, D., Turrin, F.: EVScout2. 0: electric vehicle profiling through charging profile. arXiv preprint arXiv:2106.16016 (2021)

  9. Bristow, M.: A SANS survey: OT/ICS cybersecurity, pp. 1–23 (2021). www.cisa.gov/critical-infrastructure-sectors

  10. Cazorla, L., Alcaraz, C., Lopez, J.: Cyber stealth attacks in critical information infrastructures. IEEE Syst. J. 12, 1778–1792 (2018)

    Article  Google Scholar 

  11. Chahla, C., Snoussi, H., Merghem, L., Esseghir, M.: A deep learning approach for anomaly detection and prediction in power consumption data. Energ. Effi. 13(8), 1633–1651 (2020). https://doi.org/10.1007/s12053-020-09884-2

    Article  Google Scholar 

  12. Chung, Y.W., et al.: The framework of invariant electric vehicle charging network for anomaly detection. In: IEEE Transportation Electrification Conference and Expo, ITEC, pp. 631–636 (2020)

    Google Scholar 

  13. Deloitte: Electric vehicle trends | Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/electric-vehicle-trends-2030.html. Accessed 18 May 2022

  14. Drive Dundee Electric: Electric Vehicle Charging Sessions Dundee - Datasets (2019). https://data.dundeecity.gov.uk/dataset/ev-charging-data. Accessed 08 May 2022

  15. ENISA: ENISA Threat Landscape 2021 (2021). https://doi.org/10.2824/324797, https://www.enisa.europa.eu/news/enisa-news/enisa-threat-landscape-2021

  16. Gottumukkala, R., Merchant, R., Tauzin, A., Leon, K., Roche, A., Darby, P.: Cyber-physical system security of vehicle charging stations. In: IEEE Green Technologies Conference (2019)

    Google Scholar 

  17. Hollingsworth, K., et al.: Energy anomaly detection with forecasting and deep learning. In: Proceedings of the IEEE International Conference on Big Data, Big Data, pp. 4921–4925 (2018)

    Google Scholar 

  18. Janetzko, H., Stoffel, F., Mittelstädt, S., Keim, D.A.: Anomaly detection for visual analytics of power consumption data. Comput. Graph. (Pergamon) 38(1), 27–37 (2014)

    Article  Google Scholar 

  19. Khan, O.G.M., El-Saadany, E., Youssef, A., Shaaban, M.: Impact of electric vehicles botnets on the power grid. In: IEEE Electrical Power and Energy Conference, pp. 1–5. IEEE (2019)

    Google Scholar 

  20. Amara Korba, A., Tamani, N., Ghamri-Doudane, Y., karabadji, N.E.I.: Anomaly-based framework for detecting power overloading cyberattacks in smart grid AMI. Comput. Secur. 96, 101896 (2020)

    Article  Google Scholar 

  21. Köhler, S., Baker, R., Strohmeier, M., Martinovic, I.: BROKENWIRE: wireless disruption of CCS electric vehicle charging (2022). https://www.brokenwire.fail/. Accessed 25 May 2022

  22. Li, W., Meng, W., Kwok, L.F.: Surveying trust-based collaborative intrusion detection: state-of-the-art, challenges and future directions. IEEE Commun. Surv. Tut. 24(1), 280–305 (2021)

    Article  Google Scholar 

  23. Li, Y., Ji, X., Jiang, D., Meng, T.: Abnormal detection system design of charging pile based on machine learning. IOP Conf. Ser. Earth Environ. Sci. 772(1), 012058 (2021)

    Article  Google Scholar 

  24. Li, Y., Zhang, L., Lv, Z., Wang, W.: Detecting anomalies in intelligent vehicle charging and station power supply systems with multi-head attention models. IEEE Trans. Intell. Transp. Syst. 22(1), 555–564 (2021)

    Article  Google Scholar 

  25. Lightman, S., Brewer, T.: Symposium on Federally Funded Research on Cybersecurity of Electric Vehicle Supply Equipment (EVSE) (2020). https://doi.org/10.6028/NIST.IR.8294

  26. Mishra, M.K., Dash, R.: A comparative study of chebyshev functional link artificial neural network, multi-layer perceptron and decision tree for credit card fraud detection. In: 2014 International Conference on Information Technology, pp. 228–233 (2014)

    Google Scholar 

  27. Mokhtari, S., Abbaspour, A., Yen, K.K., Sargolzaei, A.: A machine learning approach for anomaly detection in industrial control systems based on measurement data. Electronics 10(4), 407 (2021)

    Article  Google Scholar 

  28. Nejabatkhah, F., Li, Y.W., Liang, H., Reza Ahrabi, R.: Cyber-security of smart microgrids: a survey. Energies 14(1), 27 (2020)

    Article  Google Scholar 

  29. ElaadNL: Data delen @ Elaad NL (2021), https://platform.elaad.io/download-data/. Accessed 08 May 2022

  30. Open-Data Boulder Colorado: Electric Vehicle Charging Station Energy Consumption (2021). https://open-data.bouldercolorado.gov/datasets/183adc24880b41c4be9fd6a14eb6165f_0/explore. Accessed 08 May 2022

  31. Ouyang, Z., Sun, X., Chen, J., Yue, D., Zhang, T.: Multi-view stacking ensemble for power consumption anomaly detection in the context of industrial internet of things. IEEE Access 6, 9623–9631 (2018)

    Article  Google Scholar 

  32. City of Palo Alto: Electric Vehicle Charging Station Usage (July 2011–Dec 2020) \(\cdot \) Open Data \(\cdot \) City of Palo Alto (2021). https://data.cityofpaloalto.org/dataviews/257812/electric-vehicle-charging-station-usage-july-2011-dec-2020/. Accessed 08 May 2022

  33. Perth & Kinross Council: Electric Vehicle Charging Station Usage - Datasets - Perth and Kinross - Open Data (2021). https://data.pkc.gov.uk/dataset/ev-charging-data. Accessed 08 May 2022

  34. Pourmirza, Z., Walker, S.: Electric vehicle charging station: cyber security challenges and perspective. In: 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE, pp. 111–116 (2021)

    Google Scholar 

  35. Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G.: A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system. In: International Conference on Cyber Security and Protection of Digital Services, Cyber Security, pp. 1–8 (2018)

    Google Scholar 

  36. Rubio, J.E., Alcaraz, C., Lopez, J.: Addressing security in OCPP: protection against man-in-the-middle attacks. In: 9th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2018 - Proceedings, pp. 1–5 (2018)

    Google Scholar 

  37. Rubio, J.E., Manulis, M., Alcaraz, C., Lopez, J.: Enhancing security and dependability of industrial networks with opinion dynamics. In: Sako, K., Schneider, S., Ryan, P.Y.A. (eds.) ESORICS 2019. LNCS, vol. 11736, pp. 263–280. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29962-0_13

    Chapter  Google Scholar 

  38. Panda Security: Electric vehicle charging stations are vulnerable to hacker attacks (2022). https://www.pandasecurity.com/en/mediacenter/security/ev-charging-stations/. Accepted 03 May 2022

  39. Streubel, T., Kattmann, C., Eisenmann, A., Rudion, K.: Detection and monitoring of supraharmonic anomalies of an electric vehicle charging station. In: IEEE Milan PowerTech, PowerTech, pp. 1–5 (2019)

    Google Scholar 

  40. Vasilomanolakis, E., Karuppayah, S., Muhlhauser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47(4), 1–33 (2015)

    Article  Google Scholar 

  41. XGBoost: XGBoost Documentation - xgboost 1.6.0 documentation. https://xgboost.readthedocs.io/en/stable/. Accessed 22 May 2022

  42. Yandex: CatBoost - open-source gradient boosting library. https://catboost.ai/. Accessed 22 May 2022

  43. Zhang, W., Yang, Q., Geng, Y.: A survey of anomaly detection methods in networks. In: Proceedings of the 1st International Symposium on Computer Network and Multimedia Technology, CNMT, pp. 10–12 (2009)

    Google Scholar 

  44. Zhou, C.V., Leckie, C., Karunasekera, S.: A survey of coordinated attacks and collaborative intrusion detection. Comput. Secur. 29(1), 124–140 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the “Smart and Secure EV Urban Lab II” through the Second Own Plan of Smart-Campus of the University of Malaga, by the EC under the SealedGRID project (H2020-MSCA-RISE-2017) with GA no. 777996, by the Ministry of Science and Innovation under SECUREDGE project (PID2019-110565RB-I00 − AEI/10.13039/501100011033/), and by the Andalusian Government under the SAVE project (P18-TP-3724).

Author information

Authors and Affiliations

  1. Computer Science Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain

    Jesus Cumplido, Cristina Alcaraz & Javier Lopez

Authors
  1. Jesus Cumplido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Alcaraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Alcaraz .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Vijayalakshmi Atluri

  2. Hamad Bin Khalifa University, Doha, Qatar

    Roberto Di Pietro

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Christian D. Jensen

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

A Design and threats of a public charging infrastructure

A Design and threats of a public charging infrastructure

Fig. 10.
figure 10

Deployment diagram of a public charging infrastructure

Full size image

This appendix provides an overview of the components that compose a charging infrastructure and clarifies the level of susceptibility of these infrastructures to attacks according to the state of the art. Public CSs are usually managed by a CSMS, which has the ability to use ITs and OTs to efficiently control each CS and its charging sessions initialized by the users. Specifically, this control center is in charge of authenticating, authorizing and billing users, and diagnosing. Figure 10 shows a generic public charging infrastructure based on the Open Charge Point Protocol (OCPP) standard [4].

The combination of ITs and OTs in these cyber-physical systems leads to new security risks that must be considered right from the design stage. Above all, the addition of new functionalities, communications and external actors in the charging infrastructures open the door to new threats to the system. For this reason, we include in this appendix a high-level review of the state of the art [7, 16, 28] to show the susceptibility of this infrastructure to attacks and their impact on the end user and the power grid. Among the most common threats are: (T1) natural disasters, (T2) physical damage, (T3) DoS, (T4) identity theft or spoofing, (T5) malware injection, (T6) false data injection, (T7) tampering and (T8) sniffing or information disclosure.

Table 5 shows a summary of these threats with their corresponding environmental, social and economic impacts. As can be seen in the table, blackouts, economic damages and energy theft correspond to the impacts with the greatest likelihood and risk in a public charging infrastructure. This work therefore aims to mitigate these impacts through the use of Machine Learning techniques for anomaly detection. Its scope has been limited to the detection of threats related to T6 and T7, and focuses on studying the normal behavior of energy consumption data.

Table 5. Summary of threats and impacts on a public charging infrastructure
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cumplido, J., Alcaraz, C., Lopez, J. (2022). Collaborative Anomaly Detection System for Charging Stations. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds) Computer Security – ESORICS 2022. ESORICS 2022. Lecture Notes in Computer Science, vol 13555. Springer, Cham. https://doi.org/10.1007/978-3-031-17146-8_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17146-8_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17145-1

  • Online ISBN: 978-3-031-17146-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation