Deep learning-based identification of false data injection attacks on modern smart grids. (November 2022)

• Record Type: Journal Article
• Authors: Mukherjee, Debottam; Chakraborty, Samrat; Abdelaziz, Almoataz Y.; El-Shahat, Adel
• Is Part Of: Energy reports. Volume 8(2022)Supplement 15
• Page Start: 919
• Page End: 930
• Publication Date: 2022-11
• Subjects: False data injection attack -- Intrusion detection -- Smart grid -- State estimation; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.270
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: With the rapid adoption of renewables within the conventional power grid, the need of real-time monitoring is inevitable. State estimation algorithms play a significant role in defining the current operating scenario of the grid. False data injection attack (FDIA) has posed a serious threat to such kind of estimation strategies as adopted by modern grid operators by injecting malicious data within the obtained measurements. Real-time detection of such class of attacks enhances grid resiliency along with ensuring a secured grid operation. This work presents a novel real-time FDIA identification scheme using a deep learning based state forecasting model followed with a novel intrusion detection technique using the error covariance matrix. The proposed deep learning architecture with its optimum class of hyper-parameters demonstrates a scalable, real-time, effective state forecasting approach with minimal error margin. The developed intrusion detection algorithm defined on the basis of the error covariance matrix furnishes an effective real-time attack detection scheme within the obtained measurements with high accuracy. The aforementioned propositions are validated on the standard IEEE 14-bus test bench.