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

Abstract

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.