摘要：AbstractAnomaly detection is a crucial aspect of embedded applications. However, limited computational power, evolving environments, and lack of training data are difficulties that can limit anomaly detection algorithms. One class classification algorithms are often used for this task to circumvent the need of anomalous data in the training set. This paper presents a new machine learning algorithm for anomaly detection called Dynamic Double anomaly Detection DyD2that is suited to evolving environments and on-board requirements. The contributions made by DyD2are thoroughly presented and an experimental evaluation is set up to compare DyD2to state-of-the-art algorithms.
