STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems

TL;DR

This paper presents STEP-GAN, a novel multi-generator GAN training method designed to simulate cyber attacks on power systems, improving robustness and accuracy in detecting unseen threats in smart grid security applications.

Contribution

The paper introduces a step-by-step multi-generator GAN framework that enhances attack simulation and reduces mode collapse, with applications to cybersecurity in power systems.

Findings

Outperforms baseline systems by 14% and 41% in accuracy.

Reduces mode collapse in GAN training.

Robust to unseen cyber attacks.

Abstract

In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of particular importance. In recent years, however, advances in the field of machine learning, have raised concerns about cyber attacks on these systems. Power systems, among the most important components of urban infrastructure, have, for example, been widely attacked by adversaries. Attackers disrupt power systems using false data injection attacks (FDIA), resulting in a breach of availability, integrity, or confidential principles of the system. Our model simulates possible attacks on power systems using multiple generators in a step-by-step interaction with a discriminator in the training phase. As a consequence, our system is robust to unseen attacks. Moreover, the proposed model considerably reduces the well-known mode collapse problem of GAN-based models. Our method is general and it can be potentially employed in a wide range of one of one-class classification tasks. The proposed model has low computational complexity and outperforms baseline systems about 14% and 41% in terms of accuracy on the highly imbalanced publicly available industrial control system (ICS) cyber attack power system dataset.