The global deployment of the phasor measurement units (PMUs) enables
real-time monitoring of the power system, which has stimulated considerable
research into machine learning-based models for event detection and
classification. However, recent studies reveal that machine learning-based
methods are vulnerable to adversarial attacks, which can fool the event
classifiers by adding small perturbations to the raw PMU data. To mitigate the
threats posed by adversarial attacks, research on defense strategies is
urgently needed. This paper proposes an effective adversarial purification
method based on the diffusion model to counter adversarial attacks on the
machine learning-based power system event classifier. The proposed method
includes two steps: injecting noise into the PMU data; and utilizing a
pre-trained neural network to eliminate the added noise while simultaneously
removing perturbations introduced by the adversarial attacks. The proposed
adversarial purification method significantly increases the accuracy of the
event classifier under adversarial attacks while satisfying the requirements of
real-time operations. In addition, the theoretical analysis reveals that the
proposed diffusion model-based adversarial purification method decreases the
distance between the original and compromised PMU data, which reduces the
impacts of adversarial attacks. The empirical results on a large-scale
real-world PMU dataset validate the effectiveness and computational efficiency
of the proposed adversarial purification method.

通过扩散模型提出的对抗净化方法在对抗攻击下大幅提高了事件分类器的准确率，满足实时操作需求，同时减小了原始和受损的数据之间的距离，降低了对抗攻击的影响。

基于扩散模型的数据驱动电力系统事件分类器的对抗纯化

Adversarial Purification for Data-Driven Power System Event Classifiers  with Diffusion Models

There is a common observation that audio event classification is easier to
deal with than detection. So far, this observation has been accepted as a fact
and we lack of a careful analysis. In this paper, we reason the rationale
behind this fact and, more importantly, leverage them to benefit the audio
event detection task. We present an improved detection pipeline in which a
verification step is appended to augment a detection system. This step employs
a high-quality event classifier to postprocess the benign event hypotheses
outputted by the detection system and reject false alarms. To demonstrate the
effectiveness of the proposed pipeline, we implement and pair up different
event detectors based on the most common detection schemes and various event
classifiers, ranging from the standard bag-of-words model to the
state-of-the-art bank-of-regressors one. Experimental results on the ITC-Irst
dataset show significant improvements to detection performance. More
importantly, these improvements are consistent for all detector-classifier
combinations.

本文针对音频事件的检测任务，通过加入高质量事件分类器，构建了一个改进的检测管道，实现了自动化验证和拒绝虚假警报，结果表明此管道在各种检测和分类器组合下，均可显著提高检测性能。