Intrusion detection in smart grids using artificial intelligence-based ensemble modelling

Abstract For efficient distribution of electric power, the demand for Smart Grids (SGs) has dramatically increased in recent times. However, in SGs, a safe environment against cyber threats is also a concern. This paper proposes a novel Fog-based Artificial Intelligence (AI) framework for SG Networks. It uses Machine Learning (ML) and Deep Learning (DL)-based ensemble models to enhance the accuracy of detecting intrusions in SG networks. This work has two main goals, which include addressing class imbalance in network intrusion detection datasets and building interpretable models for targeted security interventions. It is achieved by using ensemble modeling, such as Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) for ML-based ensemble, while the DL ensembles consist of aggregated neural network models trained using TensorFlow. The paper assess their effectiveness in identifying malicious activities in the SG network traffic. The present study utilizes a large dataset that was custom-designed for SG intrusion detection. Most of the previous studies explored different ML techniques using a single dataset; however, the performance improvement by ensemble modeling has not been explored intensively. Therefore, this paper bridges this research gap by suggesting a novel ML-based ensemble model for intrusion detection using two datasets: CIC-IDS-Collection and a specifically designed Power System Intrusion dataset. This study has made benchmark results demonstrating the effectiveness of the proposed ensemble models for intrusion detection in SGs. Results demonstrated better accuracy, precision, recall, and F1 Scores for the proposed ensemble models over the two datasets. The accuracy, precision, recall, and F1 Scores for the proposed Ensemble model 1 for the CIC-IDS Collection dataset are 98.57%, 98.75%, 99.00%, and 98.25% and for the Power System dataset they are 98.75%, 99.05%, 99.20%, and 99.10%, respectively. Similarly, for the proposed Ensemble model 2 for the CIC-IDS Collection dataset, we have 98.84%, 99.00%, 99.00%, and 99.00% accuracy, precision, recall, and F1 Score values. For the Power System dataset, these values are 99.05%, 99.30%, 99.25%, and 99.27% for the mentioned parameters.