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Overview:

Intrusion Detection Systems (IDSs) have been widely used to overcome security threats in computer networks. Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behaviour which may result in a large number of false alarms caused by incorrect classification of events in current systems. The paper proposes a new approach of an anomaly Intrusion Detection System (IDS). It consists of building a reference behaviour model and the use of a Bayesian classification procedure associated to unsupervised learning algorithm to evaluate the deviation between current and reference behaviour. Continuous re-estimation of model parameters allows for real time operation.

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Format:PDFSize:228 KB
Date:Apr 2007
Pages:7

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