Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree

Abstract

Considering the growing trend of electric power consumption, resource constraints and the exhaustion of existing grid equipment, the issue of restructuring the electricity industry has been considered. Meanwhile, the use of Internet of Things (IoT) technology and upgrading the power grid to a Smart Grid (SG), in addition to the many benefits, poses challenges to security issues. Since Intrusion Detection System (IDS) is one of the ways forward to combat cyber-attacks, Therefore, in this paper, a smart method for intrusion detection in these types of networks is presented. In this method, a combination of three decision trees was used to detect intrusion and the performance of the proposed method was compared with the Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) methods. Experiments have been performed on the NSL-KDD dataset and the results show that the proposed method performs better than other methods for Intrusion Detection in IoT-Based SG.