An Advanced Accurate Intrusion Detection System for Smart Grid Cybersecurity Based on Evolving Machine Learning

Frontiers in Energy Research

Ying Ling¹  Dongmei Bin¹  Chunyan Yang¹  Tong Yu¹  Kai Da¹  Xin Li¹  Zhiwen Wang² 

[1] Guangxi Power Grid Co.,Ltd., Electric Power Research Institute, NanNing, China;Guangxi Power Grid Co.,Ltd., Hechi Power Supply Bureau, Hechi, China;

关键词: smart grid;    cyberattack;    intrusion detection system;    advanced machine learning;    smart city;   

DOI  :  10.3389/fenrg.2022.903370

来源: DOAJ

【 摘 要 】

Smart grids, the next generation of electricity systems, would be intelligent and self-aware of physical and cyber activity in the control area. As a cyber-embedded infrastructure, it must be capable of detecting cyberattacks and responding appropriately in a timely and effective manner. This article tries to introduce an advanced and unique intrusion detection model capable of classifying binary-class, trinary-class, and multiple-class CDs and electrical network incidents for smart grids. It makes use of the gray wolf algorithm (GWA) for evolving training of artificial neural networks (ANNs) as a successful machine learning model for intrusion detection. In this way, the intrusion detection model’s weight vectors are initialized and adjusted using the GWA in order to reach the smallest mean square error possible. With the suggested evolving machine learning model, the issues of cyberattacks, failure forecast, and failure diagnosing would be addressed in the smart grid energy sector properly. Using a real dataset from the Mississippi State Laboratory in the United States, the proposed model is illustrated and the experimental results are explained. The proposed model is compared to some of the most widely used classifiers in the area. The results show that the suggested intrusion detection model outperforms other well-known models in this field.

【 授权许可】

Unknown