论文标题
基于深度学习的工业控制系统的秘密攻击识别
Deep Learning based Covert Attack Identification for Industrial Control Systems
论文作者
论文摘要
随着数据通信越来越多地利用无线网络，工业控制系统（ICS）的网络安全正在引起重大关注。开发了许多数据驱动的方法来检测网络攻击，但很少有人专注于将它们与设备故障区分开。在本文中，我们开发了一个数据驱动的框架，该框架可用于检测，诊断和本地位置一种称为“秘密网格秘密攻击”的网络攻击类型。该框架具有混合设计，该设计结合了自动编码器，一个具有长期 - 记忆（LSTM）层的经常性神经网络（RNN）和一个深神经网络（DNN）。该数据驱动的框架考虑了通用物理系统的时间行为，该系统从传感器测量的时间序列中提取特征，可用于检测秘密攻击，将其与设备故障区分开并定位攻击/故障。我们通过对IEEE 14-BUS模型的现实模拟研究评估了所提出的方法的性能，这是IC的典型示例。我们将提出方法的性能与传统的基于模型的方法进行比较，以显示其适用性和功效。
Cybersecurity of Industrial Control Systems (ICS) is drawing significant concerns as data communication increasingly leverages wireless networks. A lot of data-driven methods were developed for detecting cyberattacks, but few are focused on distinguishing them from equipment faults. In this paper, we develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on smart grids. The framework has a hybrid design that combines an autoencoder, a recurrent neural network (RNN) with a Long-Short-Term-Memory (LSTM) layer, and a Deep Neural Network (DNN). This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault. We evaluate the performance of the proposed method through a realistic simulation study on the IEEE 14-bus model as a typical example of ICS. We compare the performance of the proposed method with the traditional model-based method to show its applicability and efficacy.
