A novel boosting-based anomaly detection scheme

Hang Hang Tong, Chong Rong Li, Jing Rui He, Quang Anh Tran, Hai Xin Duan, Xing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As a crucial issue in computer network security, anomaly detection is receiving more and more attention from both application and theoretical point of view. In this paper, by introducing boosting technique, a novel anomaly detection scheme is proposed. On the whole, the proposed scheme is based on Ada-Boost and can be viewed as an extension of Ada-Boost in terms of both probability density estimation (PDE) and confidence area estimation (CAE). Different kinds of base learners are adopted and investigated in the proposed scheme. Systematic experimental results on DARPA 1999 dataset validate the effectiveness of the proposed scheme.

Original languageEnglish (US)
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages3199-3203
Number of pages5
StatePublished - Dec 12 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: Aug 18 2005Aug 21 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Other

OtherInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
CountryChina
CityGuangzhou
Period8/18/058/21/05

Keywords

  • Anomaly detection
  • Base learner
  • Boosting
  • Confidence area estimation
  • Probability density estimation

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Tong, H. H., Li, C. R., He, J. R., Tran, Q. A., Duan, H. X., & Li, X. (2005). A novel boosting-based anomaly detection scheme. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 3199-3203). (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).