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Intrusion Detection Systems have been widely used to overcome security threats in computer networks and to identify unauthorized use, misuse, and abuse of computer systems. Anomaly-based approaches in Intrusion Detection Systems have the advantage of being able to detect unknown attacks; they look for patterns that deviate from the normal behavior. This paper proposed Hierarchical Gaussian Mixture Model (HGMM) a novel type of Gaussian Mixture which detects network based attacks as anomalies using statistical preprocessing classification. This method learns patterns of normal and intrusive activities to classify that use a set of Gaussian probability distribution functions. The use of Maximum likelihood in detection phase has used the deviation between current and reference behavior.
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