Anomaly Detection in Critical-Infrastructures using Autoencoders: A Survey

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.