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

  • Huy Anh Nguyen
  • Deokjai Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5297)

Abstract

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 different kinds of network attacks.

Keywords

Data mining Machine learning Classifier Network security Intrusion detection Algorithm selection KDD dataset 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Huy Anh Nguyen
    • 1
  • Deokjai Choi
    • 1
  1. 1.Computer Science DepartmentChonnam National UniversityGwangjuKorea

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