A Novel Data Mining Approach for Intrusion Detection in Smart Grids
Rahul Thakur · 2024
Rebuilding the electrical business has been discussed in light of the rising trend in consumption of electricity, insufficient resources, and the wear and tear on the current grid infrastructure. While there are many advantages to using Internet of Things (IoT) innovation and converting to a Smart Grid (SG), there are also concerns with regard to security. Because an intrusion detection system (IDS) is a potential strategy for fending off cyberattacks. As a result, a unique KMC-GBN (K-means Clustered - Gradient-Adaptive Bayesian Network) technique for intrusion prevention in these kinds of networks is proposed in this research. On the NSL KDD database, tests were performed. Accuracy, f-measure, Kappa, and ROC metrics are used to evaluate the performance of the suggested technique. The outcomes demonstrate that the suggested strategy for IDS for SG outperforms other methods.