Deep learning-based risk management of financial market in smart grid. (April 2022)

• Record Type: Journal Article
• Authors: Teng, Tao; Ma, Li
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Deep learning -- Cyber-attacks -- Risk management system -- Information technology -- Integrity attack -- Neural network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107844
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Deep learning-based attack diagnosing accuracy and robustness (f-score) in an asymmetric zone. An ensemble DL scheme according to DT and DNN classifiers for detecting cyber-attacks. Develop a generalized model to diverse critical infrastructure facilities with minimal changes to current systems. Utilize learning and ensemble techniques to train the system to diagnose cyber-attacks in SGCSs no matter how asymmetric the data is. 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.