• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System

Vol. 12, No. 10, October 30, 2018
10.3837/tiis.2018.10.027, Download Paper (Free):

Abstract

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.


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Cite this article

[IEEE Style]
A. Alabdallah and M. Awad, "Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System," KSII Transactions on Internet and Information Systems, vol. 12, no. 10, pp. 5143-5158, 2018. DOI: 10.3837/tiis.2018.10.027.

[ACM Style]
Alaeddin Alabdallah and Mohammed Awad. 2018. Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System. KSII Transactions on Internet and Information Systems, 12, 10, (2018), 5143-5158. DOI: 10.3837/tiis.2018.10.027.