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bakhshipour, M., namdari, F., dowlatshahi, M. (2022). A survey on detecting false data injection in power systems with auto-encoder based deep learning methods. Electronic and Cyber Defense, 10(2), 11-17.
mohammad bakhshipour; farhad namdari; mohammad bagher dowlatshahi. "A survey on detecting false data injection in power systems with auto-encoder based deep learning methods". Electronic and Cyber Defense, 10, 2, 2022, 11-17.
bakhshipour, M., namdari, F., dowlatshahi, M. (2022). 'A survey on detecting false data injection in power systems with auto-encoder based deep learning methods', Electronic and Cyber Defense, 10(2), pp. 11-17.
bakhshipour, M., namdari, F., dowlatshahi, M. A survey on detecting false data injection in power systems with auto-encoder based deep learning methods. Electronic and Cyber Defense, 2022; 10(2): 11-17.

A survey on detecting false data injection in power systems with auto-encoder based deep learning methods

پدافند الکترونیکی و سایبری
Volume 10, Issue 2 - Serial Number 38, October 2022, Pages 11-17    PDF (1.14 M)
Document Type: Original Article
Authors
mohammad bakhshipour1; farhad namdari* 2; mohammad bagher dowlatshahi3
1PhD student, Computer Education Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran
2Associate Professor, Electrical Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran
3Assistant Professor, Department of Computer Education, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran
Receive Date: 28 January 2021,  Revise Date: 05 April 2022,  Accept Date: 11 December 2021 
Abstract
The number of cyber-attacks affecting power systems and leading to physical and economic damages has grown rapidly over the last decade. Among the most significant types of cyber-attacks, are the class of false data injection attacks (FDIAs) which affect the power network monitoring systems. FDIAs endanger the power grid with manipulating the power system state estimation (SE). Also, the electricity theft has recently become another purpose of the FDAIs. Machine learning based methods are known as one of the FDIAs detection approaches. In this paper, first, using the deep auto-encoder method, the dimensions of the problem and the number of data entry for problem classification and detection are reduced. Then, by employing the support vector machine (SVM) approach and the data learning method, the procedure of cyber-attack detection is formed. Also, the precision of the proposed approach is improved by changing the
number of data being trained. The presented method is evaluated on the IEEE 14 and 118 bus systems. The obtained simulation results demonstrate that the new method can successfully be applied for an accurate and effective detection of FDIAs.
Keywords
False data; cyber-attacks; deep learning; problem dimension reduction
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