A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features...

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Bibliographic Details
Published in:MATEC web of conferences Vol. 336; p. 8008
Main Author: Xie, Tao
Format: Journal Article Conference Proceeding
Language:English
Published: Les Ulis EDP Sciences 2021
Subjects:
Algorithms
Feature selection
Genetic algorithms
Intrusion detection systems
Multiple objective analysis
ISSN:2261-236X, 2274-7214, 2261-236X
Online Access:Get full text
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Summary:In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202133608008