  Predicting and classifying faults in electricity networks is crucial for
uninterrupted provision and keeping maintenance costs at a minimum. Thanks to
the advancements in the field provided by the smart grid, several data-driven
approaches have been proposed in the literature to tackle fault prediction
tasks. Implementing these systems brought several improvements, such as optimal
energy consumption and quick restoration. Thus, they have become an essential
component of the smart grid. However, the robustness and security of these
systems against adversarial attacks have not yet been extensively investigated.
These attacks can impair the whole grid and cause additional damage to the
infrastructure, deceiving fault detection systems and disrupting restoration.
In this paper, we present FaultGuard, the first framework for fault type and
zone classification resilient to adversarial attacks. To ensure the security of
our system, we employ an Anomaly Detection System (ADS) leveraging a novel
Generative Adversarial Network training layer to identify attacks. Furthermore,
we propose a low-complexity fault prediction model and an online adversarial
training technique to enhance robustness. We comprehensively evaluate the
framework's performance against various adversarial attacks using the
IEEE13-AdvAttack dataset, which constitutes the state-of-the-art for resilient
fault prediction benchmarking. Our model outclasses the state-of-the-art even
without considering adversaries, with an accuracy of up to 0.958. Furthermore,
our ADS shows attack detection capabilities with an accuracy of up to 1.000.
Finally, we demonstrate how our novel training layers drastically increase
performances across the whole framework, with a mean increase of 154% in ADS
accuracy and 118% in model accuracy.
