Boosted Modified Probabilistic Neural Network (BMPNN) for network intrusion detection

Tich Phuoc Tran, Tony Jan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. An automated and adaptive defensive tool is imperative for computer networks. One of the emerging solutions for Network Security is the Intrusion Detection System (IDS). However, this technology still faces some challenges such as low detection rates, high false alarm rates and requirement of heavy computational power. To overcome these difficulties, this paper proposes an innovative Machine Learning algorithm called Boosted Modified Probabilistic Neural Network (BMPNN) which utilizes semi-parametric learning model and Adaptive boosting techniques to reduce learning bias and generalization variance in difficult classification. In this paper, BMPNN is implemented as a classifier to detect different types of network anomalies in the KDD-99 benchmark. Extensive experimental outcome indicates that the proposed BMPNN outperforms other state-of-the-art learning algorithms in terms of detection accuracy and model robustness at an affordable computational cost.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages2354-2361
Number of pages8
Publication statusPublished - 2006
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
Country/TerritoryCanada
CityVancouver, BC
Period16/07/0621/07/06

Keywords

  • Artificial neural network
  • Generalization variance
  • Learning bias
  • Network intrusion detection

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