在智能电网数据中优化双阶段异常检测和恢复,使用增强的DeBERTa-v3验证系统
Xiao Liao1, Wei Cui1, Min Zhang1
1State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了智能电网网络攻击检测和数据恢复的先进两级系统,显著提高了异常检测准确度和数据恢复精度. 这种新的方法提高了关键智能电网基础设施的安全性.
科学领域:
- 网络安全 网络安全
- 智能电网基础设施的建设
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能电网基础设施面临着日益复杂的网络攻击.
- 现有的异常检测和恢复系统难以平衡回忆和精度.
- 可靠的数据恢复对于运营完整性至关重要.
研究的目的:
- 开发和评估针对智能电网网络攻击的优化两阶段异常检测和恢复系统.
- 通过将增强的TimerXL探测器与基于DeBERTa-v3的验证和恢复机制相结合,提高系统性能.
- 为了在异常检测中实现高回忆和精度,同时确保准确的数据恢复.
主要方法:
- 实施了两阶段系统:第一阶段使用了基于增量的优化检测算法 (95.0%的回忆率,54.8%的精度).
- 第二阶段采用了经过修改的DeBERTa-v3架构,具有25维特征工程验证 (95.1%的精度,84.1%的回忆).
- 使用平衡损失函数 (焦点,子,对比学习),集合验证,优化样本权重,以及生成时间序列模型 (TimER) 进行恢复.
主要成果:
- 在异常检测方面获得0.873±0.114的F1得分,显著优于现有方法 (例如ARIMA,LSTM-AE,异常变压器,TimesNet).
- 恢复机制显示平均绝对误差 (MAE) 为0.0055千瓦时,比ARIMA有99.91%的改进.
- 该系统以66.6 ± 7.2毫秒的推断时间实时运行,并在各种异常大小中保持稳健的性能.
结论:
- 拟议的两阶段系统在智能电网网络安全方面取得了重大进展,提供了卓越的异常检测和数据恢复能力.
- 集成DeBERTa-v3和高级损失功能有效地解决了平衡召回和精度的挑战.
- 该系统的实时性能和稳定性使其适合在保护智能电网基础设施的运营部署.
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