Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms

Xiaoyong Bo; Zhaoyang Qu; Lei Wang; Yunchang Dong; Zhenming Zhang; Da Wang

LAPSE:2023.9523

Published Article

LAPSE:2023.9523

Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms

Xiaoyong Bo, Zhaoyang Qu, Lei Wang, Yunchang Dong, Zhenming Zhang, Da Wang

February 27, 2023

Abstract
The terminal equipment interconnection and the network communication environment are complex in power cyber−physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an active defense framework against FDIAs of power CPS based on data-driven algorithms in order to ensure that FDIAs can be efficiently detected and processed in real-time during power grid operation. First, the data transmission scenario and false data injection forms of power CPS were analyzed, and the FDIA mathematical model was expounded. Then, from a data-driven perspective, the algorithm improvement and process design were carried out for the three key links of data enhancement, attack detection, and data reconstruction. Finally, an active defense framework against FDIAs was proposed. The example analysis verified that the method proposed in this paper could effectively detect FDIAs and perform data reconstruction, providing a new idea for the active defense against FDIAs of power CPS.

Record ID

LAPSE:2023.9523

Keywords

active defense, data-driven, false data injection attacks, power cyber–physical systems, variational auto-encoder

Subject

Process Design

Suggested Citation

Bo X, Qu Z, Wang L, Dong Y, Zhang Z, Wang D. Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms. (2023). LAPSE:2023.9523

Author Affiliations

Bo X: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China; Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China; Jilin Province Engineering Technology Rese [ORCID]
Qu Z: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China; Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China
Wang L: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China; Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China
Dong Y: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China; Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China
Zhang Z: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China; Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China
Wang D: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China

Journal Name

Energies

Volume

15

Issue

19

First Page

7432

Year

2022

Publication Date

2022-10-10