The effectiveness of sampling methods for the imbalanced network intrusion detection data set

Khor, Kok Chin and Ting, Choo Yee (2014) The effectiveness of sampling methods for the imbalanced network intrusion detection data set. In: Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing . Springer International Publishing, pp. 613-622. ISBN 978-3-319-07692-8

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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...

Abstract

One of the countermeasures taken by security experts against network attacks is by implementing Intrusion Detection Systems (IDS) in computer networks. Researchers often utilize the de facto network intrusion detection data set, KDD Cup 1999, to evaluate proposed IDS in the context of data mining. However, the imbalanced class distribution of the data set leads to a rare class problem. The problem causes low detection (classification) rates for the rare classes, particularly R2L and U2R. Two commonly used sampling methods to mitigate the rare class problem were evaluated in this research, namely, (1) under-sampling and (2) over-sampling. However, these two methods were less effective in mitigating the problem. The reasons of such performance are presented in this paper.

Item Type: Book Section
Additional Information: Book Subtitle: Proceedings of The First International Conference on Soft Computing and Data Mining (SCDM-2014) Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaJune 16th-18th, 2014
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 09 Jan 2015 01:36
Last Modified: 09 Jan 2015 01:36
URI: http://shdl.mmu.edu.my/id/eprint/5922

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