  With the ever-increasing reliance on data for data-driven applications in
power grids, such as event cause analysis, the authenticity of data streams has
become crucially important. The data can be prone to adversarial stealthy
attacks aiming to manipulate the data such that residual-based bad data
detectors cannot detect them, and the perception of system operators or event
classifiers changes about the actual event. This paper investigates the impact
of adversarial attacks on convolutional neural network-based event cause
analysis frameworks. We have successfully verified the ability of adversaries
to maliciously misclassify events through stealthy data manipulations. The
vulnerability assessment is studied with respect to the number of compromised
measurements. Furthermore, a defense mechanism to robustify the performance of
the event cause analysis is proposed. The effectiveness of adversarial attacks
on changing the output of the framework is studied using the data generated by
real-time digital simulator (RTDS) under different scenarios such as type of
attacks and level of access to data.
