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IDEAL
2010
Springer

Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection

9 years 7 months ago
Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection
Intrusion detection in computer networks faces the problem of a large number of both false alarms and unrecognized attacks. To improve the precision of detection, various machine learning techniques have been proposed. However, one critical issue is that the amount of reference data that contains serious intrusions is very sparse. In this paper we present an inference process with linear chain conditional random fields that aims to solve this problem by using domain knowledge about the alerts of different intrusion sensors represented in an ontology.1
Carsten Elfers, Mirko Horstmann, Karsten Sohr, Ott
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where IDEAL
Authors Carsten Elfers, Mirko Horstmann, Karsten Sohr, Otthein Herzog
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