图表注意网络和通报器用于多变量时间序列异常检测
Mengmeng Zhao1,2,3, Haipeng Peng1,2, Lixiang Li1,2
1Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种使用图形注意网络 (GAT) 和Informer. 的工业控制系统 (ICS) 新型异常检测方法. 该方法有效地识别高维时间序列数据中的异常,增强系统安全性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 工业控制系统 工业控制系统
背景情况:
- 时间序列异常检测对于工业控制系统 (ICS) 安全至关重要.
- 现有的算法经常与高维数据作斗争,导致性能下降.
研究的目的:
- 为ICS提出一个强大的异常检测方案,克服高维数据的局限性.
- 通过先进的异常检测,提高工业控制系统的安全性和可靠性.
主要方法:
- 这是一个新的方案,它结合了图表注意网络 (GAT) 进行序列特征和Informer进行长时间序列预测.
- 利用长期和短期预测损失用于多变量时间序列异常检测.
- 在SWaT和WADI工业控制系统数据集上的实验验证.
主要成果:
- 与最先进的方法相比,取得了具有竞争力的结果,特别是在更高维度的数据集上.
- 证明了该方法在时间序列数据中准确定位异常的能力.
- 拟议的方法在异常检测中提供了可解释性.
结论:
- 基于GAT和Informer的方案为ICS的时间序列异常检测提供了有效的解决方案.
- 该方法在处理高维数据方面取得了显著的改进,提高了系统安全性.
- 这种方法既能准确地检测异常,也能对异常进行解释.
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