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，这类机器学习算法已被证明易受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，并聚焦于逻辑回归和梯度提升两类可解释分类器，以区分负荷损失与发电损失两种事件类型。由此生成的分类器随后通过对抗算法测试其鲁棒性。对抗攻击在两种场景下进行测试：白盒场景中，攻击者完全知晓分类模型；灰盒场景中，攻击者可获取与训练分类器所用网络相同的历史数据，但不知晓分类模型。在合成南卡罗来纳州500节点系统上的充分实验表明，逻辑回归等相对简单的模型比梯度提升更易受到对抗攻击。