Detecting Load Redistribution Attacks via Support Vector Models

TL;DR

This paper introduces a machine learning framework combining support vector regression and support vector machine models to detect load redistribution cyber-attacks in power systems, leveraging historical data and predicting loads to identify anomalies.

Contribution

It presents a novel detection framework that integrates load prediction and attack detection using support vector models, improving detection accuracy for load redistribution attacks.

Findings

Effective detection of load redistribution attacks demonstrated.

Attack mitigation achieved through load prediction-based re-dispatch.

Framework tested on IEEE 30-bus system with real load data.

Abstract

A machine learning-based detection framework is proposed to detect a class of cyber-attacks that redistribute loads by modifying measurements. The detection framework consists of a multi-output support vector regression (SVR) load predictor that predicts loads by exploiting both spatial and temporal correlations, and a subsequent support vector machine (SVM) attack detector to determine the existence of load redistribution (LR) attacks utilizing loads predicted by the SVR predictor. Historical load data for training the SVR are obtained from the publicly available PJM zonal loads and are mapped to the IEEE 30-bus system. The SVM is trained using normal data and randomly created LR attacks, and is tested against both random and intelligently designed LR attacks. The results show that the proposed detection framework can effectively detect LR attacks. Moreover, attack mitigation can be achieved by using the SVR predicted loads to re-dispatch generations.