SCOPUS 2005-2009

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Browsing SCOPUS 2005-2009 by Subject "Clustering"

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    A novel method for unsupervised anomaly detection using unlabelled data
    (2008) Abdul Samad Bin Haji Ismail; Abdul Hanan Abdullah; Kamalrulnizam Bin Abu Bak; Md Asri Bin Ngadi; Dahliyusmanto Dahlan; Witcha Chimphlee; A. S. B. H. Ismail; Faculty of Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; email: abdsamad@utm.my
    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.
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    An improving fuzzy ant clustering using artificial immune recognition system
    (2006) Werasak Kurutach; Surat Srinoy; Witcha Chimphlee; Siriporn Chimphlee
    We present a swarm intelligence approach to data clustering. Ant based clustering is used to initially create raw clusters and then these clusters are refined using Artificial Immune Recognition System (AIRS). AIRS that has been developed as an immune-inspired supervise learning algorithm. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose artificial immune recognition systems (AIRS) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.