Quickest Detection of Time-Varying False Data Injection Attacks in Dynamic Smart Grids


Quickest detection of false data injection attacks (FDIAs) in dynamic smart grids is considered in this paper. The unknown time-varying state variables of the smart grid and the FDIAs impose a significant challenge for designing a computationally efficient detector. To address this challenge, we propose new Cumulative-Sum-type algorithms with computational complex scaling linearly with the number of meters. Moreover, for any constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed algorithm is provided. For any given threshold employed in the proposed algorithm, an upper bound on the worstcase expected detection delay is also derived. The proposed algorithm is numerically investigated in the context of an IEEE standard power system under FDIAs, and is shown to outperform some representative algorithm in the test case.

Meeting Name

2019 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019 (2019: May 12-17, Brighton, United Kingdom)


Electrical and Computer Engineering

Keywords and Phrases

Cumulative sum; Cybersecurity; Dynamic smart grid systems; False data injection attacks

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

1520-6149; 2379-190X

Document Type

Article - Conference proceedings

Document Version


File Type





© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2019