An Anomaly Detection Framework Based on Autoencoder and Nearest Neighbor

In recent years, anomaly detection has become a focal point of data mining, and numerous efforts have been made to conduct extensive researches on the theories and techniques for detecting abnormal data points. Although the amount of anomaly data is relatively small, they can potentially bring huge losses to social economy, public resources and individual properties. Thus, we propose an unsupervised anomaly detection framework named AEKNN, which aims to incorporate the advantages of automatically learnt representation by deep neural network to boost anomaly detection performance. The framework combines the training of an autoencoder and a k-th nearest neighbor based outlier detection method. We further validate the performance of our proposed model with an extensive experimental study on three UCI datasets. The parameter sensitivity results demonstrate that the proposed algorithm can scale well with respect to both dataset size, data feature dimensionality and anomaly class proportion.