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Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic

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DOI: 10.4236/jdaip.2015.32002    4,325 Downloads   4,922 Views   Citations
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This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.

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The authors declare no conflicts of interest.

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Ammar, A. (2015) Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic. Journal of Data Analysis and Information Processing, 3, 11-19. doi: 10.4236/jdaip.2015.32002.


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