Data Mining for Enhanced Security: A Transformative Framework for Smart Grid Protection

Abstract

Smart grids fall at the intersection of conventional energy systems and modern informatics in the present digitalized energy environment. The growing number of linked devices and sensors in these networks leads to the generation of complex structures and vast quantities of data, presenting benefits and challenges. Safeguarding these complex structures against malicious intrusions and illegal activities is an important problem. The paper's main objective is to enhance smart grid security by utilizing the data mining and Artificial Intelligence (AI) approaches. As huge amounts of data are collected from the smart grids based on tiny and smart internet of things (IoT) devices, this data poses challenges as well as provides opportunities. The challenges come from analyzing this huge data, especially in real-time. At the same time, it provides opportunities to enhance the smart grid services and protection. Therefore, to overcome these challenges, this paper proposes a feedforward deep learning approach for data mining to secure the smart grid from different anomalies and allow the system to adapt to any risk it might face. Deep learning will allow the system to adjust dynamically to emerging risks. The proposed system has been examined using Power System Attack Datasets sourced from the Mississippi State University and Oak Ridge National Laboratory. The results show a detection accuracy of 91% just using 50% of the dataset features. Different percentages of the features are examined as well. However, we concluded that 50% of the features are enough for identifying the smart grid risks based on the given dataset.