A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System
| Autor: | Yucheng Ding, Tianjiao Pu, Kang Ma, Xinying Wang, Ran Li, Dongxia Zhang |
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| Rok vydání: | 2021 |
| Předmět: |
Identification scheme
TK7800-8360 Computer Networks and Communications Computer science false data injection attacks detection 02 engineering and technology computer.software_genre Deep belief network Electric power system Robustness (computer science) smart grids 0202 electrical engineering electronic engineering information engineering state estimation Electrical and Electronic Engineering conditional deep belief network Artificial neural network business.industry cyber security feature extraction Deep learning 020208 electrical & electronic engineering deep learning 020206 networking & telecommunications Support vector machine Smart grid Hardware and Architecture Control and Systems Engineering Signal Processing Artificial intelligence Data mining Electronics business computer |
| Zdroj: | Electronics, Vol 10, Iss 1459, p 1459 (2021) Electronics Volume 10 Issue 12 |
| ISSN: | 2079-9292 |
| Popis: | A smart grid improves power grid efficiency by using modern information and communication technologies. However, at the same time, due to the dependence on information technology and the deep integration of electrical components and computing information in cyber space, the system might become increasingly vulnerable to cyber-attacks. Among various emerging security problems, a false data injection attack (FDIA) is a new type of attack against the state estimation. In this article, a deep learning-based identification scheme is developed to detect and mitigate information corruption. The scheme implements a conditional deep belief network (CDBN) to analyze time-series input data and leverages captured features to detect the FDIA. The performance of our detection mechanism is validated by using the IEEE 14-bus test system for simulation. Different attack scenarios and parameters are set to demonstrate the feasibility and effectiveness of the developed scheme. Compared with the artificial neural network (ANN) and the support vector machine (SVM), the experimental analyses indicate that the results of our detection mechanism are better than those of the other two in terms of FDIA detection accuracy and robustness. |
| Databáze: | OpenAIRE |
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