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This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the...
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...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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...
This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset...
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