Autors: Asimopoulos, D.C., Radoglou-Grammatikis, P., Makris, I., Mladenov, V. M., Psannis, K.E., Goudos, S., Sarigiannidis, P.
Title: Breaching the Defense: Investigating FGSM and CTGAN Adversarial Attacks on IEC 60870-5-104 AI-enabled Intrusion Detection Systems
Keywords: Adversarial Attacks; Artificial Intelligence; Cybersecurity;

Abstract: In the digital age of the hyper-connected Critical Infrastructures (CIs), the role of the smart electrical grid is crucial, providing several benefits, such as improved grid resilience, efficient energy distribution and smart load and response management. However, despite the several advantages, the rapid evolution of the heterogeneous technologies involved in the smart electrical grid increases the attack surface. In this paper, we focus first our attention on how Artificial Intelligence (AI) can be used to protect the smart electrical grid in terms of detecting efficiently potential cyberattacks and anomalies. Secondly, we investigate how AI can be used to trick AI-enabled detection services, thus resulting in false alarms. In particular, we emphasise on cyberattacks against IEC 60870-5-104, an industrial communication protocol which is widely used in the energy domain. Therefore, a relevant AI-powered Intrusion Detection System (IDS) is provided, utilising strong Machine Learning (

References

    Issue

    18th International Conference on Availability, Reliability and Security, pp. 1-8, 2023, Italy, ARES, DOI 10.1145/3600160.3605163

    Цитирания (Citation/s):
    1. Siniosoglou, I., Asimopoulos, D., Argyriou, V., Lagkas, T., Lytos, A., Moscholios, I.D., Goudos, S.K. and Sarigiannidis, P., 2024, March. “Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo for Advanced Entity Recognition,” In 2024 Panhellenic Conference on Electronics & Telecommunications (PACET) (pp. 1-6). IEEE. DOI: 10.1109/PACET60398.2024.10497084 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    2. Bouzinis, P.S., Radoglou-Grammatikis, P., Makris, I., Lagkas, T., Argyriou, V., Papadopoulos, G.T., Sarigiannidis, P. and Karagiannidis, G.K., 2024. “StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems,” arXiv preprint arXiv:2405.13062. pp. 1-10, https://doi.org/10.48550/arXiv.2405.13062 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    3. Asimopoulos, D., Siniosoglou, I., Argyriou, V., Goudos, S.K., Psannis, K.E., Karditsioti, N., Saoulidis, T. and Sarigiannidis, P., 2024. “Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study,” 7th International Balkan Conference on Communications and Networking, BalkanCom 2024, arXiv preprint arXiv:2405.06709. ISBN 979-835036595-5, DOI 10.1109/BalkanCom61808.2024.10557182, pp. 242 - 246 (Web of Science, Scopus, Google Scholar) - 2024 - в издания, индексирани в Scopus и/или Web of Science
    4. Sinha, H., 2024, “The Identification of Network Intrusions with Generative Artificial Intelligence Approach for Cybersecurity,” JOURNAL OF Web Applications and Cyber Security, vol. 2, issue (2), e-ISSN: 2584-0908, https://doi.org/10.48001/JoWACS.2024.2220-29, pp. 20-29, (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    5. Sampedro, G.A., Ojo, S., Krichen, M., Alamro, M.A., Mihoub, A. and Karovic, V., 2024. “Defending AI Models Against Adversarial Attacks in Smart Grids Using Deep Learning,” IEEE Access. ISSN 21693536, DOI 10.1109/ACCESS.2024.3473531, pp. 157408 - 157417 (Web of Science, Scopus, Google Scholar) SJR 0.96, IF 3.4 - 2024 - в издания, индексирани в Scopus и/или Web of Science
    6. Alqhtani, M., Alghazzawi, D. and Alarifi, S., 2024. “Black-Box Adversarial Attacks Against SQL Injection Detection Model,” Contemporary Mathematics, pp. 5098-5112. ISSN 27051064, DOI 10.37256/cm.5420245292 (Web of Science, Scopus, Google Scholar) IF 0.7, SJR 0.158 - 2024 - в издания, индексирани в Scopus и/или Web of Science
    7. Asimopoulos, D.C., Radoglou-Grammatikis, P., Lagkas, T., Argyriou, V., Moscholios, I., Cani, J., Papadopoulos, G.T., Markakis, E.K. and Sarigiannidis, P., 2024, December. “AAG: Adversarial Attack Generator for evaluating the robustness of Machine Learning Models against Adversarial Attacks,” In 2024 IEEE International Conference on Big Data (BigData) (pp. 2682-2689). IEEE. DOI: 10.1109/BigData62323.2024.10826110 (Scopus, Google Scholar) - 2024 - в издания, индексирани в Scopus и/или Web of Science
    8. Radoglou-Grammatikis P., Bouzinis P.S., Makris I., Lagkas T., Argyriou V., Papadopoulos G.T., Fouliras P., Seritan G., Sarigiannidis P., AI4FIDS: Multimodal Federated Intrusion Detection, 2025, IEEE Transactions on Emerging Topics in Computing, issue 0, DOI 10.1109/TETC.2025.3562346, eissn 21686750 - 2025 - в издания, индексирани в Scopus
    9. Kurtovic H., Sabanovic E., Almisreb A.A., Saleh M.A., Ismail N., Exploring the Dark Side: A Systematic Review of Generative AI’s Role in Network Attacks and Breaches, 2025, Lecture Notes in Networks and Systems, issue 0, vol. 1273, pp. 27-51, DOI 10.1007/978-3-031-82881-2_3, issn 23673370, eissn 23673389 - 2025 - в издания, индексирани в Scopus
    10. Rathakrishnan M., Gayan S., Edirisinghe S., Inaltekin H., A Multi-Model Framework for Synthesizing High-Fidelity Network Intrusion Data Using Generative AI, 2025, 2025 5th International Conference on Advanced Research in Computing: Converging Horizons: Uniting Disciplines in Computing Research through AI Innovation, ICARC 2025 - Proceedings, issue 0, DOI 10.1109/ICARC64760.2025.10963129 - 2025 - в издания, индексирани в Scopus
    11. Deng Z., Torim A., Yahia S.B., Bahsi H., Generative AI in Intrusion Detection Systems for Internet of Things:A Systematic Literature Review, 2025, IEEE Open Journal of the Communications Society, issue 0, DOI 10.1109/OJCOMS.2025.3573194, eissn 2644125X - 2025 - в издания, индексирани в Scopus
    12. Nadjm-Tehrani, S., 2025, March. Penetrating the Power Grid: Realistic. In Critical Information Infrastructures Security: 19th International Conference, CRITIS 2024, Rome, Italy, September 18–20, 2024, Revised Selected Papers (Vol. 15549, p. 249). Springer Nature. ISSN 03029743, ISBN 978-303184259-7, DOI 10.1007/978-3-031-84260-3_15 (Google Scholar) SJR 0.352 - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    13. Antonesi, G., Cioara, T., Anghel, I., Michalakopoulos, V., Sarmas, E. and Toderean, L., 2025. “From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins,” arXiv preprint arXiv:2506.06359. https://doi.org/10.48550/arXiv.2506.06359 pp. 1-38 (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    14. Dyrmishi, S., Djilani, M., Simonetto, T., Ghamizi, S. and Cordy, M., 2025. “Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review,” arXiv preprint arXiv:2506.15506. https://doi.org/10.48550/arXiv.2506.15506, pp. 1-37 (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus