Artificial Intelligence based Approach for Identification and Mitigation of Cyber-Attacks in Wide-Area Control of Power Systems

TL;DR

This paper introduces a GAN-based deep learning approach that simultaneously detects and mitigates cyber-attacks in power system control loops, improving response speed and accuracy over existing methods.

Contribution

The paper presents an integrated LSTM-GAN framework for concurrent attack detection and mitigation in power systems, reducing response time compared to separate modules.

Findings

Effective detection of false data injection and DoS attacks

Faster response times than traditional methods

Validated on IEEE 68-bus power system model

Abstract

We propose a generative adversarial network (GAN) based deep learning method that serves the dual role of both identification and mitigation of cyber-attacks in wide-area damping control loops of power systems. Two specific types of attacks considered are false data injection and denial-of-service (DoS). Unlike existing methods, which are either model-based or model-free and yet require two separate learning modules for detection and mitigation leading to longer response times before clearing an attack, our deep learner incorporate both goals within the same integrated framework. A Long Short-Term Memory (LSTM) encoder-decoder based GAN is proposed that captures the temporal dynamics of the power system significantly more accurately than fully-connected GANs, thereby providing better accuracy and faster response for both goals. The method is validated using the IEEE 68-bus power system model.