图表注意力和Kolmogorov-Arnold基于网络的智能电网入侵检测检测入侵检测
Ying Wu1, Zhiyuan Zang2, Xitao Zou2
1School of Computer Science and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China. wuying1992@cqust.edu.cn.
Scientific reports
|March 14, 2025
概括
本研究介绍了GraphKAN,这是一种用于智能电网的新型入侵检测系统. 通过将物理和网络数据与高级图形和可学习的激活功能集成,GraphKAN提高了网络攻击检测的准确性.
科学领域:
- 网络安全 网络安全
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 电力系统的数字化转型增加了由于复杂性和互连性而导致的网络攻击风险.
- 传统的入侵检测系统难以整合物理电网数据和模拟复杂的攻击模式.
- 现有的图形神经网络 (GNN) 方法经常忽视物理设备的交互,并依赖于固定激活函数.
研究的目的:
- 为智能电网开发先进的入侵检测框架,准确捕捉复杂的设备交互和复杂的攻击模式.
- 通过整合物理和网络数据,提高关键电力基础设施入侵检测的精度.
- 克服传统GNN在表示非线性攻击行为方面的局限性.
主要方法:
- 介绍了 GraphKAN,这是一个结合 Graph Attention Network (GAT) 和 Kolmogorov-Arnold Network (KAN) 的新型框架.
- 构建了一个包括电力,IT和通信设备在内的全面图形表示,边缘表示物理和逻辑依赖.
- 采用了多头注意力的GAT来实现动态节点权重,以及可学习的B-spline激活用于增强非线性特征表达的KAN.
主要成果:
- 图形KAN实现了高的检测准确度:97.63% (二进制),98.66% (二进制) 和99.04% (37级).
- 与最先进的模型相比,显示了显著的准确性改进 (例如,与GA-RBF-SVM相比,5.73%的增长).
- 在密西西比州立大学和树国家实验室的数据集上验证了性能.
结论:
- 通过整合物理和网络信息,GraphKAN有效地提高了智能电网中的入侵检测准确性.
- 该框架在识别复杂的网络攻击模式方面表现强.
- GAT和KAN的结合为保护关键电力基础设施提供了一个强大的方法.
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