A deep learning-based classification scheme for false data injection attack detection in power system

Yucheng Ding, Kang Ma, Tianjiao Pu, Xinying Wang, Ran Li, Dongxia Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1459
JournalElectronics (Switzerland)
Volume10
Issue number12
DOIs
Publication statusPublished - 18 Jun 2021

Keywords

  • Conditional deep belief network
  • Cyber security
  • Deep learning
  • False data injection attacks detection
  • Feature extraction
  • Smart grids
  • State estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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