Intrusion Detection Using Error Correcting Output Code Based Ensemble

被引：0

作者：

AbdElrahman, Shaza Merghani ^{[1
]}

Abraham, Ajith ^{[2
,3
]}

机构：

[1] Sudan Univ Sci & Technol, Fac Comp Sci & Informat Technol, Khartoum, Sudan

[2] Machine Intelligence Res Labs MIR Labs, Sci Network Innovat & Res Excellence, Auburn, WA USA

[3] VSB Tech Univ Ostrava, Ctr excellence, IT4Innovat, Ostrava, Czech Republic

来源：

2014 14TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS) | 2014年

关键词：

Intrusion Detection; ensemble; Error Correcting Output Code (ECOC);

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：

Intrusion Detection System is an essential part in computer security. Researchers have proposed many methods but most of them suffer from low detection rates and high false alarm rates. In this paper, we try to tackle the class imbalance problem, increase detection rates for each class and minimize false alarms in intrusion detection system. We test the performance of seven classifiers using Bagging and AdaBoost ensemble methods. We proposed a new hybrid ensemble for intrusion detection based on Error Correcting Output Code (ECOC) approach.

引用

页码：181 / 186

页数：6

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