Robust Feature Extraction and Ensemble Classification Against Cyber-Physical Attacks in the Smart Grid

Abstract

Intrusion detection systems (IDS) are crucial in threats monitoring for the cyber-physical security of electrical power and energy systems in the smart grid with increasing machine-to-machine communication. However, the multi-sourced, voluminous, correlated, and often noise-contained data, which record various concurring cyber and physical events, are posing significant challenges to the accurate distinction by IDS among events of inadvertent and malignant natures. To tackle such challenges, this paper proposes a robust end-to-end framework based on Stacked Denoising Autoencoder (SDAE) and Ensemble Machine Learning to extract new noise and attack-informed feature sets from cyber-physical system data and incorporate different sources of information for reliable event classification. The proposed framework first leverages SDAE to create lower-dimensional features that allow reconstruction of a noise-free input from noise-corrupted perturbations. By combining attack and noisy inputs, we extracted new, automatically-engineered features that can preserve and present information on normal, fault, and attack events against different synthetic but realistic noises for better classification. Considering the heterogeneous nature of the inputs, which are composed of PMU measurements, system logs, and IDS alerts, we further introduced ensemble learning-based multi-classifier classification with the Extreme Gradient Boosting (XGBoost) technique to classify the samples based on the SDAE-extracted features. Normalization and oversampling were also both performed to improve the uniformity and balance of the data. On a realistic dataset of 37 sub-types of normal, fault, and attack collected from co-simulations on a hardware-in-the-loop (HIL) testbed security testbed, the results have shown that the proposed SDAE+XGBoost solution achieves over 90% classification accuracy with the SDAE features and ensemble classifiers, an effective 8% increase over the state-of-the-art.