Ensemble classifiers for supervised anomaly based network intrusion detection

被引：0

作者：

Timcenko, Valentina ^{[1
]}

Gajin, Slavko ^{[2
]}

机构：

[1] Univ Belgrade, Mihailo Pupin Inst, Sch Elect Engn, Belgrade, Serbia

[2] Univ Belgrade, Sch Elect Engn, Belgrade, Serbia

来源：

2017 13TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP) | 2017年

关键词：

network anomaly detection; intrusion; supervised machine learning; ensemble classifier; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper focuses on the problem of machine learning classifier choice for network intrusion detection, taking into consideration several ensemble classifiers from the supervised learning category. We have evaluated Bagged trees, AdaBoost, RUSBoost, LogitBoost and GentleBoost algorithms, provided an analysis of the performance of the classifiers and compared their learning capabilities, taking for the reference UNSW-NB15 dataset. The obtained results have indicated that in the defined environment and under analyzed conditions Bagged tree and GentleBoost perform with highest accuracy and ROC values, while RUSBoost has the lowest performances.

引用

页码：13 / 19

页数：7

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