Expert-based fusion algorithm of an ensemble of anomaly detection algorithms

Data fusion systems are widely used in various areas such as sensor networks, robotics, video and image processing, and intelligent system design. Data fusion is a technology that enables the process of combining information from several sources in order to form a unified picture or a decision. Today, anomaly detection algorithms (ADAs) are in use in a wide variety of applications (e.g. cyber security systems, etc.). In particular, in this research we focus on the process of integrating the output of multiple ADAs that perform within a particular domain. More specifically, we propose a two stage fusion process, which is based on the expertise of the individual ADA that is derived in the first step. The main idea of the proposed method is to identify multiple types of outliers and to find a set of expert outlier detection algorithms for each type. We propose to use semi-supervised methods. Preliminary experiments for the single-type outlier case are provided where we show that our method outperforms other benchmark methods that exist in the literature.