Intrusion detection using error correcting output code based ensemble

Author

AbdElrahman, Shaza Merghani ; Abraham, Ajith

Author_Institution

Fac. of Comput. Sci. & Inf. Technol., Sudan Univ. of Sci. & Technol., Khartoum, Sudan

fYear

2014

Firstpage

181

Lastpage

186

Abstract

Intrusion Detection System is an essential part in computer security. Researchers have proposed many methods but most of them suffer from low detection rates and high false alarm rates. In this paper, we try to tackle the class imbalance problem, increase detection rates for each class and minimize false alarms in intrusion detection system. We test the performance of seven classifiers using Bagging and AdaBoost ensemble methods. We proposed a new hybrid ensemble for intrusion detection based on Error Correcting Output Code (ECOC) approach.

Keywords

error correction codes; learning (artificial intelligence); security of data; AdaBoost ensemble method; ECOC approach; bagging ensemble; class imbalance problem; computer security; detection rate; error correcting output code approach; error correcting output code based ensemble; false alarm rate; hybrid ensemble; intrusion detection system; Accuracy; Bagging; Biological system modeling; Intrusion detection; Learning systems; Support vector machines; Vegetation; Error Correcting Output Code (ECOC); Intrusion Detection; ensemble;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2014 14th International Conference on

Print_ISBN

978-1-4799-7632-4

Type

conf

DOI

10.1109/HIS.2014.7086194

Filename

7086194