An Ensemble Approach Based on Decision Tree and Bayesian Network for Intrusion Detection
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Date
2017-06-01
Journal Title
Journal ISSN
Volume Title
Publisher
Computers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania.
Abstract
This paper presents an overview of
intrusion detection and a hybrid classification algorithm
based on ensemble method (stacking) which uses decision
tree (J48) and Bayesian network as base classifiers and
functional tree algorithm as the meta-learner. The data
set is passed through the decision tree and node Bayesian
network for classification. The meta-learner (Functional
tree classifier) will then select the value of the base
classifier that has the higher accuracy based on majority
voting. The key idea here is to always pick the value with
higher accuracy since both base classifier (decision tree
and Bayesian network) will always classify all instances.
A performance evaluation was performed using a 10-fold
cross validation technique on the individual base
classifiers (decision tree and Bayesian network) and the
ensemble classifier (DT-BN) using the KDD Cup 1999
dataset on WEKA tool. Experimental results show that the
hybrid classifier (DT-BN) gives the best result in terms of
accuracy and efficiency compared with the individual
base classifiers (decision tree and BN). The decision tree
gave a result of (99.9974% for DoS, 100% for Normal,
98.8069% for probing, 97.6021% for U2R and 73.0769%
for R2L), the Bayesian network (99.6410% for DoS,
100% for Normal, 97.1756% for probing, 97.0693% for
U2R and 69.2308% for R2L),while the ensemble method
gave a result of (99.9977% for DoS, 100% for Normal,
98.8069% for probing, 97.6909% for U2R and 73.0769%
for R2L).
Description
Keywords
Machine Learning, Data Mining, Network security, Intrusion Detection System
Citation
Balogun, A. O., Balogun, A. M., Sadiku, P. O., & Amusa, L. B. (2017): An Ensemble Method Based on Decision Tree and Bayesian Function for Intrusion Detection. Annals Computer Science Series 15th Tome 1st , Fascicle- 2017 Paper 15-1-10. 82-91