用半监督方法提升电网攻击检测准确率，降低误报。
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid
- 结合一致性正则化与不确定性伪标签，利用有标签和无标签数据。
- 在未知样本下检测准确率达99%，显著减少误报。
- 适合电力系统安全防护研究者，尤其关注低标注成本场景。
现代电网融合了数字技术和自动化系统，但这也使其易受网络攻击威胁，可能破坏数据完整性和供电可靠性。传统入侵检测系统依赖标注数据，难以有效识别新型复杂攻击。本文提出一种基于半监督学习的智能电网网络攻击检测方法，充分利用标注与未标注的测量数据。通过一致性正则化与伪标签机制识别异常行为并预测攻击类别，并采用课程学习策略优化伪标签性能，捕捉模型不确定性。实验基于公开数据集验证，该方法在未知样本下检测准确率达到99%，显著降低误报率，展现出高效检测多种攻击的能力。
原文摘要 · Abstract (English)
The modern power grids are integrated with digital technologies and automation systems. The inclusion of digital technologies has made the smart grids vulnerable to cyber-attacks. Cyberattacks on smart grids can compromise data integrity and jeopardize the reliability of the power supply. Traditional intrusion detection systems often need help to effectively detect novel and sophisticated attacks due to their reliance on labeled training data, which may only encompass part of the spectrum of potential threats. This work proposes a semi-supervised method for cyber-attack detection in smart grids by leveraging the labeled and unlabeled measurement data. We implement consistency regularization and pseudo-labeling to identify deviations from expected behavior and predict the attack classes. We use a curriculum learning approach to improve pseudo-labeling performance, capturing the model uncertainty. We demonstrate the efficiency of the proposed method in detecting different types of cyberattacks, minimizing the false positives by implementing them on publicly available datasets. The method proposes a promising solution by improving the detection accuracy to 99% in the presence of unknown samples and significantly reducing false positives.
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