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Anomaly Detection System Using Beta Mixture Models and Outlier Detection

  • Nour Moustafa
  • Gideon Creech
  • Jill Slay
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

An intrusion detection system (IDS) plays a significant role in recognising suspicious activities in hosts or networks, even though this system still has the challenge of producing high false positive rates with the degradation of its performance. This paper suggests a new beta mixture technique (BMM-ADS) using the principle of anomaly detection. This establishes a profile from the normal data and considers any deviation from this profile as an anomaly. The experimental outcomes show that the BMM-ADS technique provides a higher detection rate and lower false rate than three recent techniques on the UNSW-NB15 data set.

Keywords

Intrusion detection system (IDS) Anomaly detection system (ADS) Beta mixture model (BMM) Outlier detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.The Australian Centre for Cyber SecurityUniversity of New South WalesCanberraAustralia

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