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Volume 14
Issue 17
10.3390/electronics14173396
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking

by
Jinman Luo
1,2,
Haotian Guo
3,*,
Huichao Kong
1,
Xiaorui Hu
3,
Shimei Li
1,
Danni Zuo
3,
Guozhang Li
1,
Zhongyu Ren
3,
Yuan Li
1,
Weile Zhang
1 and
Keng-Weng Lao
3
1
Dongguan Power Supply Bureau of Guangdong Power Grid Corporation, Dongguan 523008, China
2
School of Electric Power, South China University of Technology, Guangzhou 510640, China
3
State Key Laboratory of Internet of Things for Smart City, Department of Electrical and Computer Engineering, University of Macau, Macau 999078, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(17), 3396; https://doi.org/10.3390/electronics14173396
Submission received: 18 July 2025 / Revised: 19 August 2025 / Accepted: 25 August 2025 / Published: 26 August 2025
Versions Notes

Abstract

To address the detection of stealthy False Data Injection Attacks (FDIA) that evade traditional detection mechanisms in smart grids, this paper proposes an unsupervised learning framework named SHAP-LUNAR (SHapley Additive ExPlanations-Learnable Unified Neighborhood-based Anomaly Ranking). This framework overcomes the limitations of existing methods, including parameter sensitivity, inefficiency in high-dimensional spaces, dependency on labeled data, and poor interpretability. Key contributions include (1) constructing a lightweight k-nearest neighbor graph through learnable graph aggregation to unify local anomaly detection, significantly reducing sensitivity to core parameters; (2) generating negative samples via boundary uniform sampling to eliminate dependency on real attack labels; (3) integrating SHAP for quantifying feature contributions to achieve feature-level model interpretation. Experimental results on IEEE 14-bus and IEEE 118-bus systems demonstrate F1 scores of 99.40% and 96.79%, respectively, outperforming state-of-the-art baselines. The method combines high precision, strong robustness, and interpretability.
Keywords: False Data Injection Attacks (FDIA); unsupervised learning; SHAP-LUNAR; interpretability; anomaly detection False Data Injection Attacks (FDIA); unsupervised learning; SHAP-LUNAR; interpretability; anomaly detection

Share and Cite

MDPI and ACS Style

Luo, J.; Guo, H.; Kong, H.; Hu, X.; Li, S.; Zuo, D.; Li, G.; Ren, Z.; Li, Y.; Zhang, W.; et al. False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking. Electronics 2025, 14, 3396. https://doi.org/10.3390/electronics14173396

AMA Style

Luo J, Guo H, Kong H, Hu X, Li S, Zuo D, Li G, Ren Z, Li Y, Zhang W, et al. False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking. Electronics. 2025; 14(17):3396. https://doi.org/10.3390/electronics14173396

Chicago/Turabian Style

Luo, Jinman, Haotian Guo, Huichao Kong, Xiaorui Hu, Shimei Li, Danni Zuo, Guozhang Li, Zhongyu Ren, Yuan Li, Weile Zhang, and et al. 2025. "False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking" Electronics 14, no. 17: 3396. https://doi.org/10.3390/electronics14173396

APA Style

Luo, J., Guo, H., Kong, H., Hu, X., Li, S., Zuo, D., Li, G., Ren, Z., Li, Y., Zhang, W., & Lao, K.-W. (2025). False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking. Electronics, 14(17), 3396. https://doi.org/10.3390/electronics14173396

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