基于运行状态数据链的信息物理电力系统协同网络攻击检测模型
Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link
摘要：
现有的协同网络攻击检测方法由于难以处理不平衡的攻击数据样本、高维数据和噪声数据集，检测精度和效率较低，泛化能力较差。提出了一种利用数据链进行网络和物理数据融合的模型，用于检测对网络物理电力系统（CPPS）的攻击。两步主成分分析（PCA）用于分类系统的运行状态。采用自适应合成抽样算法来减少类别样本的不平衡性。根据攻击事件的特征强度差异对损失函数进行改进，并采用基于代价敏感梯度提升决策树（CS-GBDT）的分类算法建立综合分类器。仿真结果表明，该方法比同类算法具有更高的准确率、召回率和F-Score。
英文摘要：
Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber-Physical Power System (CPPS). Two-step principal component analysis (PCA) is used for classifying the system's operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories' samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.
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