A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems

TL;DR

This paper introduces a practical adversarial attack model on smart energy systems' contingency detection, employing deep reinforcement learning to optimize attack deployment and evaluate its effectiveness on a standard IEEE 9-bus system.

Contribution

It presents the first practical adversarial attack model targeting energy system controls, optimized with deep reinforcement learning, and evaluates its impact on a standard test system.

Findings

The attack successfully compromises system controls in simulation.

Deep reinforcement learning effectively optimizes attack deployment.

The model demonstrates potential vulnerabilities in smart energy systems.

Abstract

Due to the advances in computing and sensing, deep learning (DL) has widely been applied in smart energy systems (SESs). These DL-based solutions have proved their potentials in improving the effectiveness and adaptiveness of the control systems. However, in recent years, increasing evidence shows that DL techniques can be manipulated by adversarial attacks with carefully-crafted perturbations. Adversarial attacks have been studied in computer vision and natural language processing. However, there is very limited work focusing on the adversarial attack deployment and mitigation in energy systems. In this regard, to better prepare the SESs against potential adversarial attacks, we propose an innovative adversarial attack model that can practically compromise dynamical controls of energy system. We also optimize the deployment of the proposed adversarial attack model by employing deep reinforcement learning (RL) techniques. In this paper, we present our first-stage work in this direction. In simulation section, we evaluate the performance of our proposed adversarial attack model using standard IEEE 9-bus system.