A fuzzy mutual information-based intrusion detection system for enhancing cybersecurity in smart energy grids
*Ch. Kodanda RamuCorresponding authorkvr.chintu1978@gmail.comDepartment of Computer Science and EngineeringGITAM (Deemed to Be University)Visakhapatnam, Andhra Pradesh, 530045, IndiaView full profile → , T. Srinivasa Raosthamada@gitam.eduDepartment of Computer Science and EngineeringGITAM (Deemed to Be University)Visakhapatnam, Andhra Pradesh, 530045, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Aug 2025
- Published Online:
- 25 Nov 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2150
- Pages:
- 353–369
Abstract
Keywords
Subject Classifications
References
[1] Y. Li, X. Wei, Y. Li, Z. Dong, and M. Shahidehpour, “Detection of false data injection attacks in smart grid: A secure federated deep learning approach,” arXiv preprint arXiv:2209.00778 (2022). [Online]. Available: https://arxiv.org/abs/2209.00778
[2] X. Lin, D. An, F. Cui, and F. Zhang, “False data injection attack in smart grid: Attack model and reinforcement learning-based detection method,” Frontiers in Energy Research, vol. 10, p. 1104989 (2022).
[3] X. Niu, J. Li, and J. Sun, “Dynamic detection of false data injection attack in smart grid using deep learning,” arXiv preprint arXiv:1808.01094 (2018). [Online]. Available: https://arxiv.org/abs/1808.01094
[4] K. Demertzis, L. S. Iliadis, and V.-D. Anezakis, “An innovative soft computing system for smart energy grids cybersecurity,” Advances in Building Energy Research, vol. 12, no. 1, pp. 3–24 (2018), doi: 10.1080/17512549.2017.1325401.
[5] S. Ahmad and Z. A. Baig, “Fuzzy-based optimization for effective detection of smart grid cyberattacks,” International Journal of Smart Grid and Clean Energy, vol. 1, no. 1, pp. 15–21 (2012). [Online]. Available: http://www.ijsgce.com/uploadfile/2012/1011/20121011122129685.pdf
[6] J. Wang, Q. Wang, W. Q. Ma, and D. H. Yao, “Fuzzy knowledge representation and reasoning of the smart grid based on medium logic and its application,” in Proc. 2nd Int. Conf. Computer Science and Electronics Engineering (ICCSEE), Paris, France, pp. 2786. [Online] (2013). Available: http://www.atlantis-press.com/php/download_paper.php?id=5126
[7] S. Mohagheghi, “Integrity assessment scheme for situational awareness in utility automation systems,” IEEE Transactions on Smart Grid, vol. 5, pp. 592–601 (2014), doi: 10.1109/TSG.2013.2283260.
[8] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in IEEE Symp. Computational Intelligence for Security and Defense Applications, pp. 1–6. [Online] (2009). Available: https://ieeexplore.ieee.org/document/5356528
[9] N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in MilCIS, IEEE (2015), doi: 10.1109/MilCIS.2015.7348942.
[10] U. AlHaddad, A. Basuhail, M. Khemakhem, F. E. Eassa, and K. Jambi, “Ensemble model based on hybrid deep learning for intrusion detection in smart grid networks,” Sensors, vol. 23, no. 17, p. 7464 (2023), doi: 10.3390/s23177464.
[11] F. Zhai, T. Yang, H. Chen, B. He, and S. Li, “Intrusion detection method based on CNN–GRU–FL in a smart grid environment,” Electronics, vol. 12, no. 5, p. 1164 (2023), doi: 10.3390/electronics12051164.
[12] S. A. Amaouche, A. Guezzaz, S. Benkirane, and M. Azrour, “IDS-XGbFS: A smart intrusion detection system using XGBoost with recent feature selection for VANET safety,” Cluster Computing, vol. 27, no. 3, pp. 3521–3535 (2024), doi: 10.1007/s10586-023-04157-w.
[13] M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “An intrusion detection model using election-based feature selection and K-NN,” Microprocessors and Microsystems, art. no. 104966 (2023), doi: 10.1016/j.micpro.2023.104966.
[14] M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “Malicious detection model with artificial neural network in IoT-based smart farming security,” Cluster Computing, vol. 27, no. 6, pp. 7307–7322 (2024), doi: 10.1007/s10586-024-04334-5.
[15] R. K. Viral and D. Asija, “Advanced machine learning methods for big data analytics used in smart grid,” in Big Data Analytics Framework for Smart Grids, 1st ed., R. Viral, D. Asija, and S. Salkuti, Eds. Boca Raton, FL: CRC Press, pp. 79–97 (2023), doi: 10.1201/9781032665399-5.
[16] S. Oyucu, O. Polat, M. Türkoğlu, H. Polat, A. Aksöz, and M. T. Ağdaş, “Ensemble learning framework for DDoS detection in SDN-based SCADA systems,” Sensors, vol. 24, no. 1, p. 155 (2024), doi: 10.3390/s24010155.
[17] B. R. Said, Z. Sabir, and I. Askerzade, “CNN-BiLSTM: A hybrid deep learning approach for network intrusion detection system in software-defined networking with hybrid feature selection,” IEEE Access, vol. 11, pp. 138732–138747 (2023), doi: 10.1109/ACCESS.2023.3340142.
[18] Y. Song, N. Luktarhan, Z. Shi, and H. Wu, “TGA: A novel network intrusion detection method based on TCN, BiGRU, and attention mechanism,” Electronics, vol. 12, no. 13, p. 2849 (2023), doi: 10.3390/electronics12132849.
[19] Y. Imrana, Y. Xiang, L. Ali et al., “CNN-GRU-FF: A double-layer feature fusion-based network intrusion detection system using convolution neural network and gated recurrent units,” Complex Intelligent Systems, vol. 10, pp. 3353–3370 (2024), doi: 10.1007/s40747-023-01313-y.
[20] S. Meftah, T. Rachidi, and N. Assem, “Network based intrusion detection using the UNSW-NB15 dataset,” International Journal of Computing and Digital Systems, vol. 8, no. 5, pp. 478–487 (2019).




