HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

Qize Gao; Qiuyu Lu; June Li; Zhao Peng; Guo Shen; Ming Ni

doi:10.1049/enc2.70042

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HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

更新时间：2026-07-05

- HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids
- Energy Conversion and Economics Vol. 7, Issue 3, (2026)
- 作者机构：
  
  1. Key Laboratory of Aerospace Information Security and Trusted Computing, School of Cyber Science and Engineering, Ministry of Education, Wuhan University,Wuhan,China
  2. Contribution: Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing - original draft
  3. Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing - original draft, Writing - review & editing
  4. Contribution: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing - review & editing
  5. Contribution: Conceptualization, Methodology, Supervision, Writing - review & editing
  6. School of Engineering, Mathematics and Technology, University of Bristol,Bristol,UK
  7. Contribution: Investigation, Methodology, Validation, Visualization, Writing - review & editing
  8. Leidos Engineering LLC,Chicago,USA
  9. Contribution: Methodology, Supervision, Validation, Writing - review & editing
- 作者简介：
- 基金信息：
- DOI：10.1049/enc2.70042
  CLC：
- Published：2026
- 稿件说明：
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Qize Gao, Qiuyu Lu, June Li, et al. HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids[J]. Energy Conversion and Economics, 2026, 7(3).
DOI：

Qize Gao, Qiuyu Lu, June Li, et al. HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids[J]. Energy Conversion and Economics, 2026, 7(3). DOI： 10.1049/enc2.70042.

摘要

Abstract

An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs

data from both cyber and physical domains can support attack detection. However

existing works insufficiently consider the heterogeneity

high dimensionality

and cross-domain correlations of multi-source data

affecting model generalization

stability

and detection accuracy. To address these limitations

this paper proposes a hybrid stacked learning method with cooperative feature selection (HSL-CFS). First

a cooperative feature selection approach is proposed for feature extraction from multi-source heterogeneous data. An embedded method with adaptive threshold adjustment and a wrapper method with dynamic bidirectional search extract key features from complementary perspectives

and their feature sets are merged via set union to output representative features. Second

a hybrid stacked model combining Extreme Random Trees (ET) and an improved Convolutional Neural Network (CNN) is proposed. The CNN is enhanced with Euclidean-norm regularization (L2 regularization) and Squeeze-and-Excitation (SE) block attention mechanism to mitigate overfitting and recalibrate channel responses. Furthermore

probability alignment calibrates base classifier outputs before stacking

enabling better capture of complementary patterns in SGs cyber-physical data. Experimental results show that HSL-CFS selects fewer features and achieves enhanced stability

generalization and accuracy

outperforming existing methods on the selected dataset.

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Related Author

Jaber Valinejad

Related Institution

Data and System Science Lab, Harvard University

Contribution: Conceptualization, Writing - review & editing

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