Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems
Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos · Energies · 2025
Advanced as they are, DL models in cyber-physical systems remain vulnerable to attacks like the Fast Gradient Sign Method, DeepFool, and Jacobian-Based Saliency Map Attacks, rendering system trustworthiness impeccable in applications with high stakes like power systems. In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. Nevertheless, these models are susceptible to adversarial attacks, which could lead to inaccurate predictions and system failure. In this paper, the impact of these attacks on DL models is analyzed by employing the use of defensive countermeasures such as Adversarial Training, Gaussian Augmentation, and Feature Squeezing, to investigate vulnerabilities in industrial control systems with potentially disastrous real-world impacts. Emphasizing the inherent requirement of robust defense, this initiative lays the groundwork for follow-on initiatives to incorporate security and resilience into ML and DL algorithms and ensure mission-critical AI system dependability.