NDLI: Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

Content Provider	IEEE Xplore Digital Library
Author	Hai Nguyen Franke, K. Petrovic, S.
Copyright Year	2010
Abstract	The quality of the feature selection algorithm is one of the most important factors that affects the effectiveness of an intrusion detection system (IDS). Achieving reduction of the number of relevant traffic features without negative effect on classification accuracy is a goal that greatly improves the overall effectiveness of the IDS. Obtaining a good feature set automatically without involving expert knowledge is a complex task. In this paper, we propose an automatic feature selection procedure based on the filter method used in machine learning. In particular, we focus on Correlation Feature Selection (CFS). By transforming the CFS optimization problem into a polynomial mixed 0−1 fractional programming problem and by introducing additional variables in the problem transformed in such a way, we obtain a new mixed 0 − 1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0−1 linear programming problem can then be solved by means of branch-and-bound algorithm. Our feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. The classification accuracy obtained after the feature selection by means of the C4.5 and the BayesNet machines over the KDD CUP'99 IDS benchmarking data set was also tested. Experiments show that our proposed method outperforms the best first and genetic algorithm search strategies by removing much more redundant features and still keeping the classification accuracies or even getting better performances.
Starting Page	17
Ending Page	24
File Size	228654
Page Count	8
File Format	PDF
ISBN	9781424458790
e-ISBN	9781424458806
DOI	10.1109/ARES.2010.70
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-15
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision mixed 0-1 integer linear programming Linear programming intrusion detection Classification algorithms polynomial mixed 0-1 fractional programming Genetic algorithms Filters Intrusion detection Machine learning Integer linear programming correlation-based feature selection Polynomials Testing
Content Type	Text
Resource Type	Article

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