A Convolutional Neural Network Based Approach for Cyber Intrusion Detection in Smart Grids

Smart grids, increasingly reliant on information and communication technologies (ICT), are vulnerable to complex cyberattacks, thereby mandating the deployment of intelligent and adaptable intrusion detection systems (IDS). However, the efficacy of existing IDS techniques is frequently constrained by their limited capacity to extract distinguishing features from the high-dimensional, heterogeneous data characteristic of grid operations. In order to overcome this, we suggest a novel intrusion detection model that uses a convolutional neural network (CNN) to automatically extract hierarchical features from network traffic. The suggested CNN model outperforms conventional signature-based and SVM-based techniques with an accuracy of 98.8%, precision of 98.6%, and recall of 99.3% using the KDD-CUP99 dataset. Validation on a semi-realistic dataset from the IEEE 14-bus system, which uses IEC 61850 communication protocols, shows that it is 97.3% accurate. This means that it works well when physical and cyber layers are combined. Feature importance analysis shows that cyber-layer features, such as the continuity of GOOSE sequence numbers, are very important for detection. This research introduces a feature-learning-based intrusion detection system (IDS) framework. It works well and shows potential for practical use in improving the cybersecurity of smart grids.