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Journal of Intelligent Information Systems

, Volume 48, Issue 1, pp 61–74 | Cite as

Two-tier network anomaly detection model: a machine learning approach

  • Hamed Haddad Pajouh
  • GholamHossein Dastghaibyfard
  • Sattar Hashemi
Article

Abstract

Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naïve Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.

Keywords

Anomaly detection Intrusion detection system Multi-layer classification Certainity-factor 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Hamed Haddad Pajouh
    • 1
  • GholamHossein Dastghaibyfard
    • 1
  • Sattar Hashemi
    • 1
  1. 1.Computer Science and Engineering Department, Electrical and Computer Engineering SchoolShiraz UniversityShirazIran

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