AI-Based Intrusion Detection Systems (IDS) For Securing IoT and Smart Grid Networks

The rapid expansion of Internet of Things (IoT)-Smart Grid infrastructures has been heightened in their vulnerability tendencies to diverse and evolving cyber threats, and this prompts the need for advanced intrusion detection mechanisms. Hence, this study presents a Multi-Channel Data Fusion Network (MCDFN) framework which is designed for the detection and classification of both common and domain-specific cyberattacks in real-time. The proposed architecture integrates Convolutional Neural Networks (CNN) algorithm for spatial feature extraction with recurrent layers for temporal sequence modelling which enables an effective system for recognition of both static and dynamic intrusion patterns. In the system, a dual-dataset training strategy was adopted by combining the NSL-KDD benchmark dataset with realistic IoT–Smart Grid traffic collected from Mininet-WiFi simulation environment, and this incorporated targeted attack scenarios such as Man-in-the-Middle (MITM) and Replay attacks. Furthermore, class imbalance was addressed through oversampling techniques in order to improve detection accuracy of the model for rare attack categories. Experimental evaluation of the proposed model demonstrated that the MCDFN achieved macro-averaged precision, recall, and F1-scores above 97% while maintaining a false positive rate below 1.6% across all test scenarios. Therefore, the results confirmed that the model is effective in the detection of high-frequency threats such as DoS and sophisticated low-frequency attacks without significant performance trade-offs. With respect to the high accuracy, low-latency processing and adaptability to heterogeneous network environments result, the proposed MCDFN framework represents a scalable and operationally viable intrusion detection solution for securing critical IoT–Smart Grid infrastructures against evolving cyber threats.

IDS; MCDFN; IoT; Smart Grid; Cyberattack Detection

1. Al-Garadi, M. A., Mohamed, A., Al-Ali, A., & Khan, M. (2020). A survey of machine and deep learning methods for Internet of Things (IoT) security. IEEE Communications Surveys & Tutorials, 22(3), 1646–1685. [Google Scholar] [Crossref]

2. Alsarhan, A., Alazab, M., & Alqahtani, H. (2023). Federated learning for intrusion detection in IoT networks: A comprehensive survey. Future Generation Computer Systems, 139, 1–18. [Google Scholar] [Crossref]

3. Alzahrani, B., & Alshamrani, S. (2021). Blockchain-based intrusion detection system for smart grid applications. Energies, 14(4), 1123. [Google Scholar] [Crossref]

4. Eldakhly, N. M. (2025). Optimized intrusion detection with deep learning classification models. Neural Computing and Applications. https://doi.org/10.1007/s00521-025-11383-3 [Google Scholar] [Crossref]

5. Ghosh, U., & Sanyal, S. (2021). Explainable AI for cybersecurity in smart grids. Computers & Security, 105, 102223. [Google Scholar] [Crossref]

6. Gurung, S., Ghose, M. K., & Subedi, A. (2019). Deep learning approach on network intrusion detection system using NSL-KDD dataset. International Journal of Computer Network and Information Security, 11(3), 12–20. https://doi.org/10.5815/ijcnis.2019.03.02 [Google Scholar] [Crossref]

7. Haozhe, Z. (2025). Deep Learning-Based Intrusion Detection System for Internet of Things Networks. Discover Internet of Things, 5, Article 74. [Google Scholar] [Crossref]

8. Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2022). Machine learning in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials, 24(1), 1–35. [Google Scholar] [Crossref]

9. Kakolu, S., Faheem, M. A., & Aslam, M. (2023). AI-enabled intrusion detection systems in IoT networks: Advancing defense mechanisms for resource-constrained devices. International Journal of Science and Research Archive, 9(1), 752–769. Link [Google Scholar] [Crossref]

10. Mohanty, S., Kumar, S., & Agarwal, M. (2024). Enhancing accuracy with recursive feature selection using multiple machine learning and deep learning techniques on NSL-KDD dataset. In Advances in Data-Driven Computing and Intelligent Systems (pp. 251–262). Springer. https://doi.org/10.1007/978-981-99-9518-9_18 [Google Scholar] [Crossref]

11. Nisha, M., & Udhayashri, G. (2025). AI-Powered Intrusion Detection System for IoT Security. International Journal of Science and Advanced Technology, 2(2025), 3802. Link [Google Scholar] [Crossref]

12. Nwakeze, M. O. (2024). The impact of blockchain technology on improving cybersecurity measures. International Research Journal of Modernization in Engineering Technology and Science, 6(6), 2967–2979. [Google Scholar] [Crossref]

13. Nwakeze, O. M., & Mohammed, N. U. (2025). Intelligent cyber threat detection and mitigation system for network security improvement using artificial neural network. American Journal of Sciences and Engineering Research, 8(4), 48–56. American Journal of Sciences and Engineering Research. [Google Scholar] [Crossref]

14. Revathi, S., & Malathi, A. (2013). A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research & Technology (IJERT), 2(12), 1848–1853. Retrieved from https://www.ijert.org [Google Scholar] [Crossref]

15. Sahli, Y. (2022). A comparison of the NSL-KDD dataset and its predecessor the KDD Cup ’99 dataset. International Journal of Scientific Research and Management, 10(4), EC-2022-832–839. https://doi.org/10.18535/ijsrm/v10i4.ec05 [Google Scholar] [Crossref]

16. Ullah, I., & Mahmoud, Q. H. (2022). A hybrid deep learning model for anomaly-based intrusion detection in IoT networks. Journal of Network and Computer Applications, 204, 103396. [Google Scholar] [Crossref]

17. Williamson_5. (2024). NSL-KDD Dataset. Kaggle. Retrieved from https://www.kaggle.com/datasets/williamson5/nsl-kdd-dataset [Google Scholar] [Crossref]

18. Wu, T., Fan, H., Zhu, H., You, C., Zhou, H., & Huang, X. (2022). Intrusion detection system combined enhanced random forest with SMOTE algorithm. EURASIP Journal on Advances in Signal Processing, 2022(39). https://doi.org/10.1186/s13634-022-00871-6 [Google Scholar] [Crossref]

19. Zhang, H. (2025). Development of an intelligent intrusion detection system for IoT networks using deep learning. Discover Internet of Things, 5, Article 74. Link [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet