可控制的盲点交流FDIA通过物理信息的抽象AVAEE
Siliang Zhao1, Wuman Luo2, Qin Shu1
1College of Electrical Engineering, Sichuan University, Chengdu 610000, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新的基于物理知识的AI模型,用于为电网生成现实的虚假数据注入攻击 (FDIA). 该方法增强了对估计错误的控制,并提高了对不断变化的检测方法的稳定性.
科学领域:
- 电气工程 电气工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 虚假数据注入攻击 (FDIA) 威胁到电力系统的交流状态估计,特别是在没有电压测量的情况下.
- 现有的FDIA机器学习方法在控制错误和超越培训数据的概括方面存在局限性.
研究的目的:
- 开发一种用于生成可控和隐蔽的虚假数据注入用于交流状态估计的新方法.
- 提高电力系统安全的稳定性和适应性,以防复杂的网络攻击.
主要方法:
- 提出了一个基于物理的推断式对抗变量自编码器 (PI-ExAVAE).
- 整合了来自交流电流方程的物理一致的 priors,以确保可信性和隐形性.
- 利用对抗性训练来生成现实的攻击向量.
主要成果:
- 在不同的攻击配置中绕过检测测试的成功率达到了90%.
- 与SAGAN等现有方法相比,在产生更平滑,更现实的偏差方面表现出优越的性能.
- 展示了该模型超越训练数据的推断能力,针对未见的操作场景.
结论:
- 将物理知识集成到数据驱动模型中,提高了对不断变化的检测机制的适应性和稳定性.
- PI-ExAVAE模型提供了一个强大的工具,用于为网络安全研究生成现实的和有效的FDIA.
- 基于物理知识的人工智能对于开发抗复杂网络威胁的弹性电力系统至关重要.
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