Visible to the public Anomaly Detection with Robust Deep Autoencoders

TitleAnomaly Detection with Robust Deep Autoencoders
Publication TypeConference Paper
Year of Publication2017
AuthorsZhou, Chong, Paffenroth, Randy C.
Conference NameProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4887-4
Keywordsanomaly detection, autoencoders, denoising, group robust deep autoencoder, human factors, Metrics, Pervasive Computing Security, pubcrawl, Resiliency, robust deep autoencoders, Scalability
Abstract

Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, \$X = L\_\D\ + S\$, where \$L\_\D\\$ can be effectively reconstructed by a deep autoencoder and \$S\$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.

URLhttp://doi.acm.org/10.1145/3097983.3098052
DOI10.1145/3097983.3098052
Citation Keyzhou_anomaly_2017