A novel method for unsupervised anomaly detection using unlabelled data
Date
2008
ISBN
978-076953243-1
Journal Title
Journal ISSN
Volume Title
Resource Type
Conference paper
Publisher
Journal Title
A novel method for unsupervised anomaly detection using unlabelled data
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Abstract
Most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on Principal Component Analysis (PCA) for data reduction and Fuzzy Adaptive Resonance Theory (Fuzzy ART) for classifier is presented. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Using a set of benchmark data from KDD (Knowledge Discovery and Data Mining) Competition designed by DARPA for demonstrate to detection intrusions. Experimental results show the proposed model can classify the network connections with satisfying performance. © 2008 IEEE.
Description
Citation
Proceedings - The International Conference on Computational Sciences and its Applications, ICCSA 2008