论文标题
基于交通模式分析和深度转移学习的自动生成控制系统中的异常检测
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
论文作者
论文摘要
在现代高度互连的电网中，自动发电控制（AGC）对于维持电网的稳定性至关重要。 AGC系统对信息和通信技术（ICT）系统的依赖性使其容易受到各种类型的网络攻击。因此，信息流（IF）分析和异常检测对于防止网络攻击者推动网络物理功率系统（CPP）到不稳定性而变得至关重要。在本文中，探索了CPPS中的ICT网络流量规则，并提取了ICT网络流量的频域特征，基本上是用于开发可靠的稳健学习算法，该算法可以基于Resnest卷积神经网络（CNN）学习正常的流量模式。此外，为了克服不充分标记的样本异常的问题，使用了转移学习方法。在提出的基于数据驱动的方法中，深度学习模型是通过交通频率特征训练的，这使我们的模型与AGC的参数不确定性和建模非线性训练。
In modern highly interconnected power grids, automatic generation control (AGC) is crucial in maintaining the stability of the power grid. The dependence of the AGC system on the information and communications technology (ICT) system makes it vulnerable to various types of cyber-attacks. Thus, information flow (IF) analysis and anomaly detection became paramount for preventing cyber attackers from driving the cyber-physical power system (CPPS) to instability. In this paper, the ICT network traffic rules in CPPSs are explored and the frequency domain features of the ICT network traffic are extracted, basically for developing a robust learning algorithm that can learn the normal traffic pattern based on the ResNeSt convolutional neural network (CNN). Furthermore, to overcome the problem of insufficient abnormal traffic labeled samples, transfer learning approach is used. In the proposed data-driven-based method the deep learning model is trained by traffic frequency features, which makes our model robust against AGC's parameters uncertainties and modeling nonlinearities.
