Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance. Nader, P., Honeine, P., & Beauseroy, P. In Proc. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 567 - 572, Bruges, Belgium, 22 - 24~April, 2015.
Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance [pdf]Paper  abstract   bibtex   
Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection algorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the different scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.

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