Unsupervised Learning Based Distributed Detection of Global Anomalies

Abstract

Anomaly detection has recently become an important problem in many industrial and financial applications. Very often, the databases from which anomalies have to be found are located at multiple local sites and cannot be merged due to privacy reasons or communication overhead. In this paper, a novel general framework for distributed anomaly detection is proposed. The proposed method consists of three steps: (i) building local models for distributed data sources with unsupervised anomaly detection methods, (ii) transforming local models into uniform models, and (iii) reusing learned models for new data and combining their results by considering both quality and diversity of them to detect anomalies in a global view. In experiments performed on several synthetic and real life large data sets, the proposed distributed anomaly detection method achieved prediction performance comparable or even slightly better than the global anomaly detection algorithm applied on the data set obtained when all distributed data sets were merged.