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APNOMS
2008
Springer

Application of Data Mining to Network Intrusion Detection: Classifier Selection Model

10 years 1 months ago
Application of Data Mining to Network Intrusion Detection: Classifier Selection Model
As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect diffe...
Huy Anh Nguyen, Deokjai Choi
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where APNOMS
Authors Huy Anh Nguyen, Deokjai Choi
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