Intelligent machine learning approaches are finding active use for event detection and identification that allow real-time situational awareness. Yet, such machine learning algorithms have been shown to be susceptible to adversarial attacks on the incoming telemetry data. This paper considers a physics-based modal decomposition method to extract features for event classification and focuses on interpretable classifiers including logistic regression and gradient boosting to distinguish two types of events: load loss and generation loss. The resulting classifiers are then tested against an adversarial algorithm to evaluate their robustness. The adversarial attack is tested in two settings: the white box setting, wherein the attacker knows exactly the classification model; and the gray box setting, wherein the attacker has access to historical data from the same network as was used to train the classifier, but does not know the classification model. Thorough experiments on the synthetic South Carolina 500-bus system highlight that a relatively simpler model such as logistic regression is more susceptible to adversarial attacks than gradient boosting.

翻译：智能机器学习方法正被积极用于事件检测与识别，以实现实时态势感知。然而，此类机器学习算法已被证明容易受到针对传入遥测数据的对抗攻击。本文采用基于物理的模态分解方法提取事件分类特征，并聚焦于可解释分类器，包括逻辑回归和梯度提升，以区分两类事件：负荷损失和发电损失。随后，生成的分类器通过对抗性算法进行测试，以评估其鲁棒性。对抗性攻击在两种设置下进行测试：白盒设置（攻击者确切了解分类模型）和灰盒设置（攻击者可访问与训练分类器所用同一网络的历史数据，但不知晓分类模型）。在合成南卡罗来纳500节点系统上进行的详实实验表明，逻辑回归等相对简单的模型比梯度提升更易受到对抗性攻击的影响。