FALC-IDS: Securing EVCS through Network IDS Using EDTWFS and Federated Learning from Various EV Attacks
Keywords:
Electric Vehicle Charging Stations (EVCS); Network Intrusion Detection; Federated Learning; Cybersecurity; CIC EV Charger Attack Dataset, SVM, RF, ANN, CNN, EDTWFS, FALC.Abstract
In united states around 2.5 million electric vehicles are operating nowadays. They required the Electric vehicle charging stations (EVCS). EVCS has personal and payment information, and it run different protocols than traditional firewalls. To protect against cyber-attacks, a new Intrusion Detection Model is required. However, new vulnerabilities are still found. This work proposed a novel Federated Averaging Learning Classifier (FALC) for intrusion detection in EV charging stations (EVCS). This work uses FALC, it allows various clients to train models without sharing data. The FALC-IDS used CIC EV Charger Attack Dataset 2024 (CICEVSE2024). It contains network traffic, hardware performance, and kernel event data. The dataset is pre-processed to remove unwanted content, such as missing /duplicate values and unwanted columns. An Enhanced Dynamic Threshold Whale Optimization based Feature Selection (EDTWFS) is used to choose best features (SF) from the dataset. Also, Set of Novel features (NF) are created using statistical methods. The FALC model is compared with CNN, SVM and ANN. Experimental results show that the FALC model outperforms the other models in terms of accuracy and F1-score. On the SF dataset, it has an accuracy of 99.98% and an F1-score of 99.77%. On the NF dataset, it has an accuracy of 99.86% and an F1-score of 99.73%.
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ElKashlan, M.; Aslan, H.; Said Elsayed, M.; Jurcut, A.D.; Azer, M.A. Intrusion Detection for Electric Vehicle Charging Systems (EVCS). Algorithms 2023, 16, 75. https://doi.org/10.3390/a16020075
Zhaoliang Lin, Jinguo Li, FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile, The Computer Journal, 2023.
Mukhtar, B.I.; Elsayed, M.S.; Jurcut, A.D.; Azer, M.A. IoT Vulnerabilities and Attacks: SILEX Malware Case Study. Symmetry 2023, 15, 1978. https://doi.org/10.3390/sym15111978
J. Cumplido, C. Alcaraz, and J. Lopez, “Collaborative anomaly detection system for charging stations”, The 27th European Symposium on Research in Computer Security (ESORICS 2022) vol. 13555, pp. 716736, 2022. http://doi.org/https://doi.org/10.1007/978-3-031-17146-8_35 [5] Samrat Acharya, Hafiz Anwar Ullah Khan, Ramesh Karri, and Yury Dvorkin, "MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation", arXiv:2311.06226v1 [eess.SY] 10 Nov 2023
Dustin Kern, Christoph Krauß, Matthias Hollick, "Detection of Anomalies in Electric Vehicle Charging Sessions", ACSAC '23: Annual Computer Security Applications Conference, Austin, TX, USA, December 2023, DOI: https://doi.org/10.1145/3627106.3627127
Hamdare S, Kaiwartya O, Aljaidi M, Jugran M, Cao Y, Kumar S, Mahmud M, Brown D, Lloret J. Cybersecurity Risk Analysis of Electric Vehicles Charging Stations. Sensors (Basel). 2023 Jul 27;23(15):6716. doi: 10.3390/s23156716. PMID: 37571500; PMCID: PMC10422437.
ElKashlan, M.; Elsayed, M.S.; Jurcut, A.D.; Azer, M. A, “Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs)”, Electronics, 2023, 12, 1044. https://doi.org/10.3390/electronics12041044
M. Basnet and M. Hasan Ali, "Deep Learning-based Intrusion Detection System for Electric Vehicle Charging Station," 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), Bangkok, Thailand, 2020, pp. 408-413, doi: 10.1109/SPIES48661.2020.9243152.
Vinod Miskin, P. Chandaragi and U. V Wali, "Intrusion Detection System for Electric Vehicle Charging Station," 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, India, 2023, pp. 1-7, doi: 10.1109/ICMNWC60182.2023.10435897.
Balakrishna, A. Kumar, T. V, A. Younas, N. M. G. Kumar and R. Rastogi, "A Novel Ensembling of CNN-A-LSTM for IoT Electric Vehicle Charging Stations Based on Intrusion Detection System," 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp. 1312-1317, doi: 10.1109/ICSSAS57918.2023.10331735.
N. Singh and R. Agarwal, "Intrusion Detection System for Smart Vehicles Using Machine Learning Algorithms," 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), Greater Noida, India, 2023, pp. 749-753, doi: 10.1109/ICCSAI59793.2023.10421298.
E. D. Buedi, A. A. Ghorbani, S. Dadkhah, and R. Ferreira. “Enhancing EV Charging Station Security Using A Multi-dimensional Dataset: CICEVSE2024”. - (Submitted to ESORICS 2024 Conference).
Amanoul, S.V.; Abdulazeez, A.M. Intrusion detection system based on machine learning algorithms: A review. In Proceedings of the 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), Selangor, Malaysia, 12 May 2022; pp. 79–84.
Balaji, R.; Deepajothi, S.; Prabaharan, G.; Daniya, T.; Karthikeyan, P.; Velliangiri, S. Survey on intrusions detection system using deep learning in iot environment. In Proceedings of the 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 7–9 April 2022; pp. 195–199.
Zeadally, S.; Tsikerdekis, M. Securing internet of things (iot) with machine learning. Int. J. Commun. Syst. 2020, 33, e4169.
Ying, X. An overview of overfitting and its solutions. In Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2019; Volume 1168, p. 022022.
Ahmad, R.; Alsmadi, I.; Alhamdani, W.; Tawalbeh, L. A comprehensive deep learning benchmark for iot ids. Computer. Security, 2022, 114, 102588.
D.V. Jeyanthi and B. Indrani, "An Efficient Intrusion Detection System with Custom Features using FPA-Gradient Boost Machine Learning Algorithm", International Journal of Computer Networks & Communications (IJCNC), Vol.14, Issue.1, pp.99-115,2022.
T.S. Urmila, Dr. R. Balasubramanian, "Dynamic Multi-layered Intrusion Identification and Recognition using Artificial Intelligence Framework", International Journal of Computer Science and Information Security (IJCSIS), Vol. 17, No. 2, pp.137-147, 2019.
G.B. Govindaprabhu, M. Sumathi, "Ethno medicine of Indigenous Communities: Tamil Traditional Medicinal Plants Leaf detection using Deep Learning Models", Procedia Computer Science, Volume 235, 2024, Pages 1135-1144, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2024.04.108.
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