在自动生成控制系统中基于随机子空间组合检测虚假数据注入攻击
1Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Heliyon
|December 6, 2024
概括
机器学习有效地检测到智能电网自动生成控制 (AGC) 系统中的虚假数据注入 (FDI) 攻击. 样本对样本的特征和随机子空间集合分类器在识别网络威胁方面显示出高准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 智能电网自动发电控制 (AGC) 系统容易受到网络攻击,特别是虚假数据注入 (FDI),威胁到电力系统的可靠性.
- 现有的基于模型的外国直接投资检测方法有局限性.
研究的目的:
- 引入和评估基于机器学习 (ML) 的技术,用于在AGC系统中准确地检测FDI攻击.
- 为了比较FDI检测不同信号特征的有效性.
主要方法:
- 对三个信号特征的分析:原始离散,循环到循环和样本到样本.
- 模拟各种FDI攻击 (步骤,脉冲,随机) 和AGC系统的正常负载变化.
- 四个ML分类器用于FDI攻击分类的比较.
主要成果:
- 基于样本对样本的特征在区分外国直接投资攻击时明显优于其他特征.
- 随机子空间组合 (RaSE) 分类器,使用样本对样本特征,准确识别所有正常和外国直接投资攻击场景.
- 机器学习技术为传统的外国直接投资检测方法提供了一个有希望的替代方案.
结论:
- 机器学习,特别是具有样本对样本功能和RaSE分类器,为增强AGC系统中FDI攻击检测提供了强大的解决方案.
- 这种方法可以克服传统基于模型的检测技术的局限性,提高电力系统的安全性.
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