Semi-Supervised Outlier Detection and Deep Feature Extraction for Detecting Cyber-Attacks in Smart Grids Using PMU Data

Abstract

Smart grids are facing many challenges including cyber-attacks which can cause devastating damages to the grids. Existing machine learning based approaches for detecting cyber-attacks in smart grids are mainly based on supervised learning, which needs representative instances from various attack types to obtain good detection models. In this paper, we investigated semi-supervised outlier detection algorithms for this problem which only use instances of normal events for model training. Data collected by phasor measurement units (PMUs) was used for training the detection model. The semi-supervised outlier detection algorithms were augmented with deep feature extraction for enhanced detection performance. Our results show that semi-supervised outlier detection algorithms can perform better than popular supervised algorithms. Deep feature extraction can significantly improve the performance of semi-supervised algorithms for detecting cyber-attacks in smart grids.