Anomaly Detection in Critical-Infrastructures using Autoencoders: A Survey
Harindra S. Mavikumbure, Chathurika S. Wickramasinghe, Daniel Marino, Victor Cobilean, Milos Manic
Abstract
In critical infrastructures, timely detection of anomalies is essential to detect failures, avoid catastrophic damages, and improve resilience. Neural Network models are one of the state-of-the-art approaches used for anomaly detection. Among Neural Network architectures used these days, Autoencoders (AEs) have gained significant attention due to their advantages such as unsupervised learning, dimensionality reduction, non-linear feature extraction, the ease of integration with other neural network algorithms, and ease of use. Therefore, in this paper, we present: 1) anomaly detection and types of anomaly detection, 2) recent advancements in AEs typically used in anomaly detection, 3) AE-based Anomaly Detection (AE-AD) in selected critical infrastructures such as smart grids, intelligent transportation systems, and smart buildings, and 4) future research opportunities. We hope that this systematic survey of AE-based anomaly detection approaches will help the community prioritize research efforts to address pressing issues in critical infrastructures.