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An SVM-Based Ensemble Approach for Intrusion Detection

Santosh Kumar Sahu, Akanksha Katiyar, Kanchan Mala Kumari, Govind Kumar, Durga Prasad Mohapatra

Source Title: International Journal of Information Technology and Web Engineering (IJITWE)14(1)

ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781522564485|DOI: 10.4018/IJITWE.2019010104

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MLA

Sahu, Santosh Kumar, et al. "An SVM-Based Ensemble Approach for Intrusion Detection." IJITWE vol.14, no.1 2019: pp.66-84. http://doi.org/10.4018/IJITWE.2019010104

APA

Sahu, S. K., Katiyar, A., Kumari, K. M., Kumar, G., & Mohapatra, D. P. (2019). An SVM-Based Ensemble Approach for Intrusion Detection. International Journal of Information Technology and Web Engineering (IJITWE), 14(1), 66-84. http://doi.org/10.4018/IJITWE.2019010104

Chicago

Sahu, Santosh Kumar, et al. "An SVM-Based Ensemble Approach for Intrusion Detection," International Journal of Information Technology and Web Engineering (IJITWE) 14, no.1: 66-84. http://doi.org/10.4018/IJITWE.2019010104

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Abstract

The objective of this article is to develop an intrusion detection model aimed at distinguishing attacks in the network. The aim of building IDS relies on upon preprocessing of intrusion data, choosing most relevant features and in the plan of an efficient learning algorithm that properly groups the normal and malicious examples. In this experiment, the detection model uses an ensemble approach of supervised (SVM) and unsupervised (K-Means) to detect the patterns. This technique first divides the data and forms two clusters as per K-Means and labels the clusters using the Support Vector Machine (SVM). The parameters of K-Means and SVM are tuned and optimized using an intrusion dataset. The SVM provides up to 88%, and K-Means provides up to 83% accuracy individually. However, the ensemble of K-Means and SVM provides more than 99% on three benchmarked datasets in less time. The SVM only classifies three instances of each cluster randomly and labels them as per a majority voting approach. The proposed approach outperforms compared to earlier ensemble approaches on intrusion datasets.

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An SVM-Based Ensemble Approach for Intrusion Detection

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