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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Vydané v:MATEC web of conferences Ročník 336; s. 8008
Hlavný autor: Xie, Tao
Médium: Journal Article Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Les Ulis EDP Sciences 2021
Predmet:
Algorithms
Feature selection
Genetic algorithms
Intrusion detection systems
Multiple objective analysis
ISSN:2261-236X, 2274-7214, 2261-236X
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Abstract 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.
AbstractList 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.
Author Xie, Tao
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Cites_doi 10.1016/j.comnet.2020.107183
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SubjectTerms Algorithms
Feature selection
Genetic algorithms
Intrusion detection systems
Multiple objective analysis
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