Deep learning-based risk management of financial market in smart grid

Abstract

Smart grid control systems (SGCSs) become more vulnerable to cyber-attacks because of the combination of the Internet of Things and communication systems. Conventional intrusion detection systems (IDSs) that have been essentially improved in order to secure information technology systems. Since SGCS datasets are asymmetric, the majority of IDSs suffer from poor precision and significant false-positive rates. A deep learning (DL) layout for constructing novel symmetric presentations of the asymmetric datasets is proposed in the present study. It is incorporated into a model created particularly for detecting attacks using DL in a SGCS environment. Deep Neural Networks and Decision Tree classifiers are utilized in the suggested attack detection model. By performing 10-fold cross-validation using 2 actual SGCS datasets, this suggested model has been assessed for its efficiency. According to the outcomes, the suggested approach is more effective than traditional schemes such as Random Forest, Support Vector Machine.