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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System.

Xiao Liao1, Wei Cui1, Min Zhang1

  • 1State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced two-stage system for smart grid cyberattack detection and data recovery, significantly improving anomaly detection accuracy and data restoration precision. The novel approach enhances security for critical smart grid infrastructure.

Keywords:
DeBERTa-v3TimERcyber-attack detectiondata recoveryensemble verificationgenerative modelssmart grid securitytime series analysistransformer architecturetwo-stage anomaly detection

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Area of Science:

  • Cybersecurity
  • Smart Grid Infrastructure
  • Artificial Intelligence
  • Machine Learning

Background:

  • Smart grid infrastructure faces increasingly sophisticated cyberattacks.
  • Existing anomaly detection and recovery systems struggle to balance recall and precision.
  • Reliable data restoration is crucial for operational integrity.

Purpose of the Study:

  • To develop and evaluate an optimized two-stage anomaly detection and recovery system for smart grid cyberattacks.
  • To enhance system performance by combining an enhanced TimerXL detector with a DeBERTa-v3-based verification and recovery mechanism.
  • To achieve high recall and precision in anomaly detection while ensuring accurate data restoration.

Main Methods:

  • Implemented a two-stage system: Stage 1 uses an optimized increment-based detection algorithm (95.0% recall, 54.8% precision).
  • Stage 2 employs a modified DeBERTa-v3 architecture with 25-dimensional feature engineering for verification (95.1% precision, 84.1% recall).
  • Utilized a balanced loss function (focal, Dice, contrastive learning), ensemble verification, optimized sample weighting, and a generative time series model (TimER) for recovery.

Main Results:

  • Achieved an F1-score of 0.873 ± 0.114 for anomaly detection, significantly outperforming existing methods (e.g., ARIMA, LSTM-AE, Anomaly Transformer, TimesNet).
  • The recovery mechanism demonstrated a Mean Absolute Error (MAE) of 0.0055 kWh, a 99.91% improvement over ARIMA.
  • The system operates in real-time with a 66.6 ± 7.2 ms inference time and maintains robust performance across various anomaly magnitudes.

Conclusions:

  • The proposed two-stage system offers a significant advancement in smart grid cybersecurity, providing superior anomaly detection and data recovery capabilities.
  • The integration of DeBERTa-v3 and advanced loss functions effectively addresses the challenges of balancing recall and precision.
  • The system's real-time performance and robustness make it suitable for operational deployment in protecting smart grid infrastructure.