论文标题
通过支持向量模型检测负载重新分布攻击
Detecting Load Redistribution Attacks via Support Vector Models
论文作者
论文摘要
提出了一个基于机器学习的检测框架来检测一类通过修改测量值重新分配负载的网络攻击。检测框架由多输出支持矢量回归（SVR）负载预测器组成，该预测因子通过利用空间和时间相关来预测负载，以及随后的支持向量机（SVM）攻击检测器确定负载重新分配（LR）攻击的存在，以确定使用SVR预测器预测负载的攻击。用于培训的历史负载数据SVR是从公开可用的PJM区域负载中获得的，并映射到IEEE 30-BUS系统。使用普通数据和随机创建的LR攻击对SVM进行了训练，并针对随机和智能设计的LR攻击进行了测试。结果表明，提出的检测框架可以有效地检测LR攻击。此外，通过使用SVR预测的负载重新划分来实现攻击缓解。
A machine learning-based detection framework is proposed to detect a class of cyber-attacks that redistribute loads by modifying measurements. The detection framework consists of a multi-output support vector regression (SVR) load predictor that predicts loads by exploiting both spatial and temporal correlations, and a subsequent support vector machine (SVM) attack detector to determine the existence of load redistribution (LR) attacks utilizing loads predicted by the SVR predictor. Historical load data for training the SVR are obtained from the publicly available PJM zonal loads and are mapped to the IEEE 30-bus system. The SVM is trained using normal data and randomly created LR attacks, and is tested against both random and intelligently designed LR attacks. The results show that the proposed detection framework can effectively detect LR attacks. Moreover, attack mitigation can be achieved by using the SVR predicted loads to re-dispatch generations.
