Universiti Teknologi Malaysia Institutional Repository

Data reduction and ensemble classifiers in intrusion detection

Zainal, Anazida and Maarof, Mohd Aizaini and Shamsuddin, Siti Mariyam (2008) Data reduction and ensemble classifiers in intrusion detection. In: Proceedings of the IEEE Second Asia International Conference on Modelling & Simulation, May 13-15, 2008, Kuala Lumpur, Malaysia.

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Official URL: http://dx.doi.org/10.1109/AMS.2008.146

Abstract

Efficiency is one of the major issues in intrusion detection. Inefficiency is often attributed to high overhead and this is caused by several reasons. Among them are continuous detection and the use of full feature set to look for intrusive patterns in the network packet. The purpose of this paper are; to address the issue of continuous detection by introducing traffic monitoring mechanism and a lengthy detection process by selectively choose significant features to represent a network connection. In traffic monitoring, a new recognition paradigm is proposed in which it minimizes unnecessary recognition. Therefore, the purpose of traffic monitoring is two-folds; to reduce amount of data to be recognized and to avoid unnecessary recognition. Empirical results show 30 to 40 percent reduction of normal connections is achieved in DARPA KDDCup 1999 datasets. Finally we assembled Adaptive Neural Fuzzy Inference System and Linear Genetic Programming to form an ensemble classifiers. Classification results showed a small improvement using the ensemble approach for DoS and R2L classes.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:intrusion detection, data reduction, neural networks
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:5601
Deposited By: PM Mazleena Salleh
Deposited On:27 May 2008 03:42
Last Modified:01 Jun 2010 15:32

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