A data-driven preprocessing scheme on anomaly detection in big data applications

Abstract

Efficient anomaly detection mechanisms are becoming an urgent and critical topic in the presence of big data applications. In this paper, we propose a data-driven preprocessing scheme on anomaly detection that incorporates a dimensionality reduction algorithm and present a real-time learning idea for big data applications. Specifically, we make extensive use of the robust data preprocessing and a real-time data learning approach. The proposed robust data preprocessing scheme not only preserves the critical property of dimensionality reduction for high-dimensional data, but also introduces a robust detection boundary to the presence of outliers. The real-time learning method is inspired by online learning, which differs from batch based data processing that performs data learning on an entire batch of data set. Real-time learning aims to make progress with each example it looks at. Detailed discussions are provided for the justification of this scheme. A case study is presented to demonstrate the feasibility of the application of the proposed scheme.