Eduardo J. Spinosa
ABSTRACT
In this paper, a cluster-based novelty detection technique capable of dealing with a large amount of data is presented and evaluated in the context of intrusion detection. Starting with examples of a single class that describe the normal profile, the proposed technique detects novel concepts initially as cohesive clusters of examples and later as sets of clusters in an unsupervised incremental learning fashion. Experimental results with the KDD Cup 1999 data set show that the technique is capable of dealing with data streams, successfully learning novel concepts that are pure in terms of the real class structure.
- C. Elkan. Results of the KDD'99 classifier learning. ACM SIGKDD Explorations, 1(2):63--64, 2000. Google Scholar
Digital Library
- M. Markou and S. Singh. Novelty detection: a review - part 1: statistical approaches. Signal Processing, 83:2481--2497, 2003. Google ScholarDigital Library
- E. J. Spinosa, A. P. L. F. de Carvalho, and J. Gama. OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams. In 22nd Annual ACM Symposium on Applied Computing (SAC 2007), pages 448--452. ACM, 2007. Google ScholarDigital Library
Index Terms
- Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks
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