Lu Kang-Di, Zhou Le, Wu Zheng-Guang
IEEE Trans Neural Netw Learn Syst. 2024 May;35(5):6145-6155. doi: 10.1109/TNNLS.2023.3257225. Epub 2024 May 2.
Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.
受益于通信网络、计算单元和控制系统的进步，预计信息物理电力系统（CPPS）将成为复杂而智能的系统，其中会出于各种目的生成、交换和处理大量数据。由于这些强大的交互作用，CPPS将引入新的安全漏洞。为确保CPPS的安全运行和控制，检测受攻击测量的位置并消除由恶意网络攻击（如虚假数据注入攻击、干扰攻击、拒绝服务攻击或混合攻击）引起的状态偏差至关重要。因此，本文在基于表示学习的卷积神经网络（RL-CNN）用于CPPS的智能攻击定位和系统恢复方面做出了首个贡献。在所提出的方法中，将网络攻击的位置检测问题表述为CPPS的多标签分类问题。最初采用RL-CNN作为多标签分类器来探索和利用测量的隐含信息。通过与先前的多标签分类器进行比较，RL-CNN提高了复杂CPPS攻击定位的性能。然后，为自动过滤用于系统恢复的网络攻击，使用均方估计器来处理去除受污染测量后的状态估计困难。在该方案中，基于随机潮流的输出或历史测量来获取系统状态的先验知识。在三个IEEE总线系统中的大量仿真结果表明，所提出的方法能够为攻击定位提供高精度，并在各种网络攻击下为系统恢复执行自动攻击过滤。
