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An Ensemble of classification techniques for Intrusion Detection Systems

Authors: MAITANMI Olusola , Adebola , & AJAYI Olutayo

Publication Type: Journal article

Journal: Nternational Journal Of Computer Science And Information Security (ijcsis),

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Abstract

Extenuating intrusions into a network has become a great concern for network security scholars as they pose a threat to the confidentiality, integrity and availability of the data stored as well as derogating the services rendered by the network. Several researchers have proposed diverse techniques in other to curb intrusions into a network using various mechanisms. One of the mechanisms used is data mining. However, some of these systems have high false positive rates and relatively low detection rates which signifies a flaw in the system. In other to drastically reduce false positive rate and achieve higher detection rate whilst maintaining computational efficiency, a stacking ensemble using random forest, naïve bayes and c4.5 classifiers as base learners and support vector machine as the meta learner was proposed. The proposed stacking ensemble has a detection rate of 99.5% and a false positive rate of 0.6%. Compared to existing frameworks, the proposed ensemble performed better in detecting intrusions.

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MAITANMI ,O. Adebola,A. AJAYI,O. . (2019). An Ensemble of classification techniques for Intrusion Detection Systems, 17 (), 24-24.

MAITANMI ,O. Adebola,A. AJAYI,O. . "An Ensemble of classification techniques for Intrusion Detection Systems" 17, no (), (2019): 24-24.

MAITANMI ,O. and Adebola,A. and AJAYI,O. and . (2019). An Ensemble of classification techniques for Intrusion Detection Systems, 17 (), pp24-24.

MAITANMI O, AdebolaA, AJAYIO, . An Ensemble of classification techniques for Intrusion Detection Systems. 2019, 17 ():24-24.

MAITANMI ,Olusola , Adebola,Alaba , and AJAYI,Olutayo . "An Ensemble of classification techniques for Intrusion Detection Systems", 17 . (2019) : 24-24.

M.Olusola A.Alaba & A.Olutayo , "An Ensemble of classification techniques for Intrusion Detection Systems" vol.17, no., pp. 24-24, 2019.

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