An unsupervised cyberattack detection scheme for AC microgrids using Gaussian process regression and one‐class support vector machine anomaly detection
An unsupervised cyberattack detection scheme for AC microgrids using Gaussian process regression and one‐class support vector machine anomaly detection
复制标题
使用高斯过程回归和一类支持向量机异常检测的交流微电网无监督网络攻击检测方案
DOI：
10.1049/rpg2.12753
复制
发表时间：
2023
影响因子：
2.6
通讯作者：
Bidram, Ali
中科院分区：
文献类型：
--
作者：
Choi, Jeewon;Roshanzadeh, Behshad;Martínez‐Ramón, Manel;Bidram, Ali
研究了交流微网分布式二次控制分级控制的网络安全问题。假设虚假数据注入(FDI)网络攻击改变了孤岛微电网中基于逆变器的分布式发电机(DG)的运行频率。对于由并网逆变器和二次控制以分布式方式运行的微电网，对一个分布式电源的攻击不仅会恶化相应的分布式电源，而且还会恶化通过分布式通信网络接收损坏信息的其他分布式电源。为此，提出了一种基于高斯过程回归和单类支持向量机异常检测相结合的FDI攻击检测算法。该算法是无监督的，因为它不需要标记的异常数据来进行训练，这是很难收集的。高斯过程模型预测DG的响应，其预测误差和估计方差提供给OC-支持向量机异常检测器。与独立的OC-支持向量机相比，该算法具有更好的检测性能。所提出的网络攻击检测器使用从4DG微电网测试模型收集的数据进行训练和测试，并在模拟和半实物测试床上进行了验证。
This paper addresses the cybersecurity of hierarchical control of AC microgrids with distributed secondary control. The false data injection (FDI) cyberattack is assumed to alter the operating frequency of inverter‐based distributed generators (DGs) in an islanded microgrid. For the microgrids consisting of the grid‐forming inverters with the secondary control operating in a distributed manner, the attack on one DG deteriorates not only the corresponding DG but also the other DGs that receive the corrupted information via the distributed communication network. To this end, an FDI attack detection algorithm based on a combination of Gaussian process regression and one‐class support vector machine (OC‐SVM) anomaly detection is introduced. This algorithm is unsupervised in the sense that it does not require labelled abnormal data for training which is difficult to collect. The Gaussian process model predicts the response of the DG, and its prediction error and estimated variances provide input to an OC‐SVM anomaly detector. This algorithm returns enhanced detection performance than the standalone OC‐SVM. The proposed cyberattack detector is trained and tested with the data collected from a 4 DG microgrid test model and is validated in both simulation and hardware‐in‐the‐loop testbeds.
影响因子：
9.6
作者：
Mustafa, Aquib;Poudel, Binod;Modares, Hamidreza
通讯作者：
Modares, Hamidreza