Coordinated data falsification attack detection in the domain of distributed generation using deep learning
Coordinated data falsification attack detection in the domain of distributed generation using deep learning
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使用深度学习在分布式发电领域协调数据伪造攻击检测
DOI：
10.1016/j.ijepes.2021.107345
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发表时间：
2022
影响因子：
5.2
通讯作者：
Sengupta, Shamik
中科院分区：
文献类型：
--
作者：
Bhusal, Narayan;Gautam, Mukesh;Shukla, Raj Mani;Benidris, Mohammed;Sengupta, Shamik
本文提出了一种基于深度学习的多标签分类方法，用于检测对大量分布式生成器（DG）协调且同时发起的数据伪造攻击。所提出的方法可以检测协调加法、演绎法以及加法和演绎法的组合（攻击者使用加法和演绎攻击的组合来伪装他们的攻击）类型的功率输出操纵和对 DG 的伪造攻击。在训练所提出的分类器时，DG 仪表的读数、监控和数据采集 (SCADA) 系统的数据以及气象数据用作输入，类别标签（加法、演绎和组合）用作输出。输出类别标签是根据 DG 的正常输出和受损输出之间的比较而制定的。开发了两个具有单独类别标签的并行数据伪造分类器，以提高检测精度。所提出的方法在多个系统上进行了演示，包括 240 节点真实分布式系统（位于美国）和 IEEE 123 节点分布式测试系统。结果表明，所提出的方法可以检测低裕度协调攻击（低至实际 DG 读数的 5%），准确率高达 99.9%。该工作的性能与多层感知器（MLP）、卷积神经网络（CNN）和残差神经网络进行了比较。该解决方案的所有开发源代码（包括 OpenDSS-MATLAB 环境中的不平衡准静态潮流和 Python 中的深度学习）均已在 GitHub 上公开提供。
This paper proposes a deep learning-based multi-label classification approach to detect coordinated and simultaneously launched data falsification attacks on a large number of distributed generators (DGs). The proposed approach can detect coordinated additive, deductive, and combination of additive and deductive (attackers use the combination of additive and deductive attacks to camouflage their attacks) types of power output manipulation and falsification attacks on DGs. In training the proposed classifier, readings from DG meters and data from supervisory control and data acquisition (SCADA) systems along with meteorological data are used as input and class labels (additive, deductive, and combination) are used as output. The output class labels are developed based on the comparison between normal and compromised outputs of DGs. Two parallel data falsification classifiers with separate class labels are developed to increase the detection accuracy. The proposed approach is demonstrated on several systems including a 240-node real distribution system (based in the USA) and the IEEE 123-node distribution test system. The results show that the proposed approach can detect low margin coordinated attacks (as low as 5% of actual DG readings) with up to 99.9% accuracy. The performance of the proposed work is compared with multi-layer perceptron (MLP), convolutional neural network (CNN), and residual neural network. All of the developed source codes (including unbalanced quasi-static power flow in OpenDSS-MATLAB environment and deep learning in Python) of the proposed solution are publicly available at GitHub.
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