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Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same...
A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion...
We propose in this paper an automated feature weighting method based on fuzzy subspace approach to assign a weight to each network feature depending on its degree of importance in anomaly detection. Fuzzy c-means and fuzzy entropy modeling are used to calculate weight values and k-means vector quantization is used to model network patterns. The proposed method not only increases the detection rate...
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