Comparative Performance Analysis of Neural Network–Based Techniques for Botnet Detection in Smart Grid Cyber-Physical Systems

The increasing integration of information and communication technologies into smart power grids has significantly improved operational efficiency but has also introduced critical cybersecurity vulnerabilities. Among emerging threats, botnet attacks pose a serious risk to smart grid cyber-physical systems due to their distributed, adaptive, and coordinated nature. This study presents a comparative performance analysis of three neural network–based techniques, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) networks, for detecting botnet-induced anomalies in smart grid environments. To guarantee fairness and repeatability, the models were trained and assessed using a publicly accessible smart-grid cyberattack dataset under similar preprocessing and experimental settings. Standard assessment measures, such as accuracy, precision, recall, F1-score, convergence behavior, and confusion matrix analysis, were used to gauge performance. The findings show that DNN produces better accuracy and overall classification stability through richer feature representations, whereas ANN offers a robust baseline with good recall. Because it can incorporate temporal relationships in smart-grid data, the LSTM model performs superior to both ANN and DNN on all measures, exhibiting greater accuracy and balanced detection capabilities. These results show that temporally aware deep learning models provide notable benefits for detecting coordinated botnet attacks and emphasize the significance of model architecture choices in cyber-physical intrusion detection. The study provides valuable insights for researchers and practitioners seeking effective data-driven security solutions for protecting critical smart grid infrastructure.

Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) networks, Smart-grid, Network Anomaly

1. Abdullahi, M., Alhussian, H., Aziz, N., Abdulkadir, S. J., Alwadain, A., Muazu, A. A., & Bala, A. (2024). Comparison and investigation of AI-based approaches for cyberattack detection in cyber-physical systems. IEEE Access, 12, 31988–32004. https://doi.org/10.1109/ACCESS.2024.3370436 [Google Scholar] [Crossref]

2. Abou-Elasaad, M. M., Sayed, S. G., & El-Dakroury, M. M. (2025). Smart grid intrusion detection system based on AI techniques. Journal of Cybersecurity & Information Management, 15(2), 195. https://doi.org/10.54216/JCIM.150215 [Google Scholar] [Crossref]

3. Al-Shetwi, Ali Q., et al. (2025) "Latest advancements in smart grid technologies and their transformative role in shaping the power systems of tomorrow: An overview." Progress in Energy 7.1: 012004. [Google Scholar] [Crossref]

4. Atıcı, S., & Tuna, G. (2025). Impact of cybersecurity attacks on electrical system operation. In Cyber Security Solutions for Protecting and Building the Future Smart Grid (pp. 117-160). Elsevier. [Google Scholar] [Crossref]

5. Bhuiyan, T. (2025). AI in Smart Grid Cybersecurity: A Systematic Review of Machine Learning and Deep Learning Approaches against False Data Injection and Other Emerging Attacks. Journal of Computer Science and Technology Studies, 7(8), 1207-1295. [Google Scholar] [Crossref]

6. Dayarathne, M. A. S. P., Jayathilaka, M. S. M., Bandara, R. M. V. A., Logeeshan, V., Kumarawadu, S., & Wanigasekara, C. (2025). Mitigating cyber risks in smart cyber-physical power systems through deep learning and hybrid security models. IEEE Access, 13, 37474–37492. https://doi.org/10.1109/ACCESS.2025.3545637 [Google Scholar] [Crossref]

7. Diaba, S. Y., Shafie-khah, M., & Elmusrati, M. (2022). On the performance metrics for cyber-physical attack detection in smart grid. Soft Computing, 26(23), 13109–13118. https://doi.org/10.1007/s00500-022-06761-1 [Google Scholar] [Crossref]

8. Hnamte, V., Najar, A. A., Nhung-Nguyen, H., Hussain, J., & Sugali, M. N. (2024). DDoS attack detection and mitigation using deep neural network in SDN environment. Computers & Security, 138, 103661. [Google Scholar] [Crossref]

9. Hussain, A., Yadav, A., & Ravikumar, G. (2024). Anomaly detection using bi-directional long short-term memory networks for cyber-physical electric vehicle charging stations. IEEE Transactions on Industrial Cyber-Physical Systems. https://doi.org/10s.1109/TICPS.2024.3437349 [Google Scholar] [Crossref]

10. Imtiaz, N., Wahid, A., Ul Abideen, S. Z., Kamal, M. M., Sehito, N., Khan, S., Virdee, B. S., Kouhalvandi, L., & Alibakhshikenari, M. (2025). A deep learning-based approach for the detection of various Internet of Things intrusion attacks through optical networks. Photonics, 12(1), 35. https://doi.org/10.3390/photonics12010035 [Google Scholar] [Crossref]

11. Krichen, M., & Mihoub, A. (2025). Long short-term memory networks: A comprehensive survey. AI, 6(9), 215. [Google Scholar] [Crossref]

12. Maiti, S., & Dey, S. (2024). Smart grid security: A verified deep reinforcement learning framework to counter cyber-physical attacks. arXiv. https://doi.org/10.48550/arXiv.2409.15757 [Google Scholar] [Crossref]

13. Manias, D. M., Saber, A. M., Radaideh, M. I., Gaber, A. T., Maniatakos, M., Zeineldin, H., ... & El-Saadany, E. F. (2024). Trends in smart grid cyber-physical security: Components, threats and solutions. IEEE Access. [Google Scholar] [Crossref]

14. Patel, N. K., Anagha, N., & BJ, S. K. (2024). Effective intrusion detection and prevention system of botnet attack in blockchain technology using recurrent neural network. In Proceedings of the IEEE Conference (pp. 1–6). IEEE. https://doi.org/10.1109/ciscon62171.2024.10696133 [Google Scholar] [Crossref]

15. Ren, S., Chen, S., & Zhang, Q. (2025). Autonomous Threat Detection and Response through Actor–Critic Reinforcement Learning. [Google Scholar] [Crossref]

16. Sagar, M., & Chandrasekaran, V. (2025). An exploration of utilising deep learning models in the realm of cyber-physical systems. International Journal of Services, Economics and Management, 16(4/5), 578–606. https://doi.org/10.1504/IJSEM.2025.148484 [Google Scholar] [Crossref]

17. Sahani, N., Zhu, R., Cho, J. H., & Liu, C. C. (2023). Machine learning-based intrusion detection for smart grid computing: A survey. ACM Transactions on Cyber-Physical Systems, 7(2), 1-31. [Google Scholar] [Crossref]

18. Suresh Babu, G. N. (2024). A Novel ANN-Based Support Vector Machine for improving Classification Accuracy in Intrusion Detection Systems. Journal of Computational Analysis & Applications, 33(2). [Google Scholar] [Crossref]

19. Ullah, F., Ullah, I., Khan, K., Khan, S., & Amin, F. (2025). Advances in deep neural network-based hyperspectral image classification and feature learning with limited samples: a survey. Applied Intelligence, 55(6), 370. [Google Scholar] [Crossref]

20. Xie, R., Wang, B., & Xu, X. (2025). A novel federated deep learning for intrusion detection in smart grid cyber-physical systems. Engineering Applications of Artificial Intelligence, 162(Part B), Article 112404. https://doi.org/10.1016/j.engappai.2025.112404 [Google Scholar] [Crossref]

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