False Data Injection Attacks Detection Based on Stacking and MIC-DCXGB

Tong Li, Tian Xia (), Haoming Zhang, Dongyang Liu, Hai Zhao and Zhuolin Liu
Additional contact information
Tong Li: School of Computer and Engineering, Northeastern University, Shenyang 110169, China
Tian Xia: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Haoming Zhang: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Dongyang Liu: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Hai Zhao: School of Computer and Engineering, Northeastern University, Shenyang 110169, China
Zhuolin Liu: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Sustainability, 2024, vol. 16, issue 22, 1-13

Abstract: With the integration of sustainable energy, the power grid has become increasingly information-intensive and complex. To address the issue of power grid cyber-physical systems being unable to operate securely and stably when systems suffer false data injection attacks, a two-stage detection method based on Stacking and Maximum Information Coefficient and Dual-layer Confidence Extreme Gradient Boosting (MIC-DCXGB) is proposed by the paper. Firstly, a Stacking classification model consisting of multiple heterogeneous learners detects anomalies in real-time measurement data samples to determine if false data are present. Secondly, the method incorporates the Maximum Information Coefficient (MIC) for feature selection, which non-linearly measures the correlation between data features and fairly removes redundant features by evaluating the amount of information one feature variable contains about another. This approach effectively tackles the high-dimensional redundancy problem commonly faced in false data injection attack detection. Then, the paper introduces a dual-layer confidence Extreme Gradient Boosting (XGBoost) tree with positive feedback information transmission to classify node states. By combining grid topology learning with label correlation, it selectively uses preceding label information to reduce errors in the predictions learned by subsequent classifiers, achieving precise localization of the attack positions. Finally, extensive simulations validate the effectiveness of the proposed method.

Keywords: stacking; MIC-DCXGB; FDIA; distributed network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/22/9692/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/22/9692/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:22:p:9692-:d:1515805

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().