Detecting false data attacks using machine learning techniques in smart grid: A survey

Cui, Lei, Qu, Youyang, Gao, Longxiang, Xie, Gang and Yu, Shui 2020, Detecting false data attacks using machine learning techniques in smart grid: A survey, Journal of Network and Computer Applications, vol. 170, pp. 1-15, doi: 10.1016/j.jnca.2020.102808.

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Title Detecting false data attacks using machine learning techniques in smart grid: A survey
Author(s) Cui, Lei
Qu, Youyang
Gao, LongxiangORCID iD for Gao, Longxiang orcid.org/0000-0002-3026-7537
Xie, Gang
Yu, Shui
Journal name Journal of Network and Computer Applications
Volume number 170
Article ID 102808
Start page 1
End page 15
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2020-11
ISSN 1084-8045
Keyword(s) Smart grid
Security
False data
Machine learning
Intrusion detection
Summary The big data sources in smart grid (SG) enable utilities to monitor, control, and manage the energy system effectively, which is also promising to advance the efficiency, reliability, and sustainability of energy usage. However, false data attacks, as a major threat with wide targets and severe impacts, have exposed the SG systems to a large variety of security issues. To detect this threat effectively, several machine learning (ML)-based methods have been developed in the past few years. In this paper, we provide a comprehensive survey of these advances. The paper starts by providing a brief overview of SG architecture and its data sources. Moreover, the categories of false data attacks followed by data security requirements are introduced. Then, the recent ML-based detection techniques are summarized by grouping them into three major detection scenarios: non-technical losses, state estimation, and load forecasting. At last, we further investigate the potential research directions at the end of the paper, considering the deficiencies of current ML-based mechanisms. Specifically, we discuss intrusion detection against adversarial attacks, collaborative and decentralized detection framework, detection with privacy preservation, and some potential advanced ML techniques.
Language eng
DOI 10.1016/j.jnca.2020.102808
Indigenous content off
Field of Research 0805 Distributed Computing
1005 Communications Technologies
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141042

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