An Ensemble Approach Based on Decision Tree and Bayesian Network for Intrusion Detection

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dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorBalogun, Adedayo Miftaudeen
dc.contributor.authorSadiku, Peter Ogirima
dc.contributor.authorAmusa, Lateef
dc.date.accessioned2018-05-23T10:44:45Z
dc.date.available2018-05-23T10:44:45Z
dc.date.issued2017-06-01
dc.description.abstractThis 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).en_US
dc.identifier.citationBalogun, 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. 82-91en_US
dc.identifier.issn2065-7471
dc.identifier.urihttp://hdl.handle.net/123456789/271
dc.language.isoenen_US
dc.publisherComputers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania.en_US
dc.relation.ispartofseriesVolume: 15;Issue: 1
dc.subjectMachine Learningen_US
dc.subjectData Miningen_US
dc.subjectNetwork securityen_US
dc.subjectIntrusion Detection Systemen_US
dc.titleAn Ensemble Approach Based on Decision Tree and Bayesian Network for Intrusion Detectionen_US
dc.typeArticleen_US

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