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通过使用空间和时间拓信息来对付恶意攻击的新型检测机制
Qingfeng Wu1, Shufa Zhuang1, Xinyu Wang2,3
1College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China.
Scientific reports
|March 23, 2025
概括
这项研究引入了一种用于检测电力系统中虚假数据攻击 (FDA) 的新方法. 拟议的时空特征框架有效识别恶意数据,增强电网安全.
科学领域:
- 网络物理系统安全 网络物理系统安全
- 电力系统工程 电力系统工程
- 人工智能应用的人工智能应用.
背景情况:
- 由于集成的网络物理网络,现代电力系统面临安全风险.
- 虚假数据攻击 (FDA) 可以破坏传统的检测机制,如奇方位检测器.
- 现有的方法可能很难有效地识别复杂的数据操纵.
研究的目的:
- 为电力系统中虚假数据攻击 (FDA) 提出一个新的检测框架.
- 通过先进的机器学习技术,提高FDA检测的准确性和可靠性.
- 解决当前针对复杂网络威胁的检测方法的局限性.
主要方法:
- 提出了一个基于时空特征的检测框架.
- 开普勒优化算法 (KOA) 为空间特征提取优化卷积神经网络 (CNN).
- 带有注意力机制的双向门反复单元 (BiGRU) 提取时间特征.
- 该框架结合了KOA-CNN用于空间和BiGRU-Attention用于时间特征分析.
主要成果:
- 拟议的KOA-CNN-BiGRU-Attention框架证明了正常和异常数据的有效识别.
- 在IEEE 14-bus和118-bus系统上的模拟验证了框架的性能.
- 与GCN和GGNN-GAT相比,该模型显示了改进的准确性,精度,F1得分和回忆.
结论:
- 开发的时空检测框架为电力系统中的FDA提供了强大的解决方案.
- 优化的CNN和BIGRU与注意力机制显著提高了检测能力.
- 这种方法为确保关键电力基础设施的安全提供了一个有希望的方向.
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