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Intrusion Detection System based on Support Vector Machines and the Two-Phase Bat Algorithm



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Journal of Information Systems Security
Volume 13, Number 3 (2017)
Pages 135149
ISSN 1551-0123 (Print)
ISSN 1551-0808 (Online)
Eseoghene Daniel Erigha — Department of Computer Science, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
Femi Emmanuel Ayo — Department of Computer Science, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
Oluwatobi Olakunle Dada — Department of Computer Science, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
Olusegun Folorunso — Department of Computer Science, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
Information Institute Publishing, Washington DC, USA




Network Intrusions have become a pervasive threat to online ecosystems. Hence the need for an effective Intrusion Detection System (IDS) to safeguard and protect assets from a myriad of network attacks. A number of IDS that utilize effective feature selection methods have been proposed in the literature. However, this study asserts that an IDS can provide better performance if parameter optimization for classifier is embedded in the feature selection process. Consequently, this paper proposes a hybrid wrapper feature selection approach that combines Binary Bat algorithm with Lévy flights, together with Bat algorithm and Support Vector machines (BBAL-BA-SVM). The Binary Bat algorithm with Lévy flight performs the feature selection while the Bat algorithm performs parameter optimization on the SVM for each feature subset. Experimental results using NSL-KDD dataset prove that the proposed model provides higher accuracy in attack detection with lower false alarm rate over compared models.




Intrusion Detection System, Bat Algorithm, Binary Bat Algorithm, Support Vector Machines (SVM), BBAL




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