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ISSN 1996-1065 [Online]
ISSN 1818-1139 [PRINT] |
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Volume 2 (1) 2008 |
| Title: | Network Intrusion Detection Based On Rough Set And K-Nearest Neighbour |
| Authors: | Adebayo O. Adetunmbi*, Samuel O. Falaki, Olumide S. Adewale and Boniface K. Alese |
| Published: | ŠIJCIR Vol2 (1) 2008, PP. 60-66 |
| Language: | English |
Abstract:
Increasing numbers of interconnected networks to the internet have led to an increase in cyber attacks
which necessitates the need for an effective intrusion detection system. In this paper, two machine
learning techniques: Rough Set (LEM2 Algorithm) and k-Nearest Neighbour (kNN) are used for
intrusion detection. Rough set is a classic mathematical tool for feature extraction in a dataset which
also generates explainable rules for intrusion detection. The experimental study is done on the
international Knowledge Discovery and Data mining tools competition (KDD) dataset for
benchmarking intrusion detection systems. In the entire experimentations, we compare the performance
of Rough Set with k-Nearest Neighbour. The results generated from the experiment reveal that knearest
neighbour has a better performance in terms of accuracy but consumes more memory and
computational time. Rough Sets classifies at relative short time and employs simple explainable rules.
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| General Terms: | Keywords: Rough set, intrusion detection, nearest neighbour |
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