Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems
Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems
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DOI：
10.1109/tsg.2022.3148233
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发表时间：
2022-05
影响因子：
9.6
通讯作者：
Qi Li;Jinan Zhang;Junbo Zhao;Jin Ye;Wenzhan Song;Fangyu Li
中科院分区：
文献类型：
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作者：
Qi Li;Jinan Zhang;Junbo Zhao;Jin Ye;Wenzhan Song;Fangyu Li
由于包含分布式可再生能源发电，为有源配电系统制定网络安全策略具有挑战性。本文提出了一种自适应分层网络攻击检测和定位框架的分布式主动配电系统，通过分析电波形。网络攻击检测基于顺序深度学习模型，通过该模型，即使是轻微的网络攻击也可以识别。两阶段网络攻击定位算法首先估计网络攻击子区域，然后在估计的子区域内定位指定的网络攻击。我们提出了一种改进的基于谱聚类的网络划分方法的分层网络攻击的“粗”定位。接下来，为了进一步缩小网络攻击位置，提出了基于波形统计度量的归一化影响分数，以通过表征不同的波形属性来获得“精细”网络攻击位置。最后，与经典和最新方法相比，通过两个案例研究进行了全面的定量评估，表明所提出的框架具有良好的估计结果。
Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.