DSTF-GKAN:在动态智能电网中进行实时窃听检测的轻量级时空融合框架
Rong Wang1, Yanjin Shen1, Dongtao Wang1
1Hunan Automotive Engineering Vocational University, Zhuzhou, Hunan, China.
PloS one
|August 22, 2025
概括
这项研究引入了一个新的框架来检测智能电网中的窃听,显著提高了准确性并减少了错误. 轻量化设计可实现安全能源互联网的实时边缘防御.
科学领域:
- 网络安全
- 无线通信网络
- 智能电网
背景情况:
- 智能电网和电力物联网 (PIoT) 面临着动态窃听威胁.
- 传统的检测方法在复杂的拓变化和非线性特征方面失败.
- 需要高效,轻量化和适应性的安全解决方案.
研究的目的:
- 提出一个动态时空融合框架 (DSTF-GKAN),用于检测PIOT中的动态窃听攻击.
- 将图形循环神经网络 (GRNN) 与科尔莫戈罗夫-阿诺德网络 (KAN) 集成,以提高检测能力.
- 开发一个高效,强大的智能电网安全解决方案.
主要方法:
- DSTF-GKAN框架将GRNN的时空建模与KAN的自适应线近似相结合.
- 适应性网格优化和层次稀疏规范化用于参数压缩.
- 整合了物理层安全 (PLS) 特性限制,以提高异常检测的灵敏度.
主要成果:
- 在动态攻击场景下,DSTF-GKAN的检测F1得分为0.891,比GRNN提高了7.1% (AMR=0.5).
- 局部化误差 (RMSE) 降低了16. 2%,至0. 518米.
- 优化模型大小为0.2 MB,推断延迟为0.9毫秒,边缘设备的能耗为16mJ.
结论:
- DSTF-GKAN为智能电网安全提供了高效,强大和可解释的检测框架.
- 轻量级设计支持实时边缘防御, 这对于安全的能源互联网生态系统至关重要.
- 废除研究证实了GRU- GCN模块和PLS调节的有效性.
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