Universiti Teknologi Malaysia Institutional Repository

Attribute normalization techniques and performance of intrusion classifiers: a comparative analysis

Ihsan, Zohair and Idris, Mohd. Yazid and Abdullah, Abdul Hanan (2013) Attribute normalization techniques and performance of intrusion classifiers: a comparative analysis. Life Science Journal, 10 (4). pp. 2568-2576. ISSN 1097-8135

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Abstract

Network traffic have several attributes with different range of values. These attributes can be qualitative or quantitative in nature. Attributes with large values significantly influence the performance of intrusion classifier making it bias towards them. Attribute normalization eliminates such dominance of the attributes by scaling the values of all the attributes within a specific range. The paper discusses various normalization techniques and their influence on intrusion classifiers such as Random Forest, Bayes Net, Naive Bayes, NB Tree and Decision Tree. Furthermore, the concept of hybrid normalization is applied by normalizing the qualitative and quantitative attributes differently. Experiments on KDD Cup 99 suggests that the hybrid normalization can achieve better results as compared to conventional normalization.

Item Type:Article
Uncontrolled Keywords:attribute normalization, intrusion detection
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:48948
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:02 Dec 2015 02:09
Last Modified:30 Nov 2018 06:43

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