An in-depth experimental study of anomaly detection using gradient boosted machine View Full Text


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Article Info

DATE

2017-07-05

AUTHORS

Bayu Adhi Tama, Kyung-Hyune Rhee

ABSTRACT

This paper proposes an improved detection performance of anomaly-based intrusion detection system (IDS) using gradient boosted machine (GBM). The best parameters of GBM are obtained by performing grid search. The performance of GBM is then compared with the four renowned classifiers, i.e. random forest, deep neural network, support vector machine, and classification and regression tree in terms of four performance measures, i.e. accuracy, specificity, sensitivity, false positive rate and area under receiver operating characteristic curve (AUC). From the experimental result, it can be revealed that GBM significantly outperforms the most recent IDS techniques, i.e. fuzzy classifier, two-tier classifier, GAR-forest, and tree-based classifier ensemble. These results are the highest so far applied on the complete features of three different datasets, i.e. NSL-KDD, UNSW-NB15, and GPRS dataset using either tenfold cross-validation or hold-out method. Moreover, we prove our results by conducting two statistical significant tests which are yet to discover in the existing IDS researches. More... »

PAGES

1-11

References to SciGraph publications

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  • 2015-05. Deep learning in NATURE
  • 2017-02. Two-tier network anomaly detection model: a machine learning approach in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2015. A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems in ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2016. Performance Analysis of Multiple Classifier System in DoS Attack Detection in INFORMATION SECURITY APPLICATIONS
  • 2016. Improving the Accuracy of Intrusion Detection Using GAR-Forest with Feature Selection in PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON FRONTIERS IN INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2015
  • 2017-12. An effective combining classifier approach using tree algorithms for network intrusion detection in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s00521-017-3128-z

    DOI

    http://dx.doi.org/10.1007/s00521-017-3128-z

    DIMENSIONS

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