Multi-objective Functions in Particle Swarm Optimization for Intrusion Detection

被引：0

作者：

Cleetus, Nimmy ^{[1
]}

Dhanya, K. A. ^{[1
]}

机构：

[1] SCMS Sch Engn & Technol, Dept Comp Sci & Engn, Ernakulam, Kerala, India

来源：

2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) | 2014年

关键词：

Particle Swarm Optimization; multi-objective functions; intrusion detection; fitness function;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The paper constitutes the study of particle swarm optimization in multi-objective functions. Swarm intelligence plays a vital role in intrusion detection. Intrusion detection system identifies the normal as well as abnormal behavior of a system. The weighted aggregation method is considered as multi-objective functions. We propose an intrusion detection mechanism based on particle swarm optimization which has a strong global search capability and used for the dimensionality optimization. Random forest is used as the classifier for modelling attacks and legitimate set. An accuracy of 91.71% at detection time of 0.22sec is obtained.

引用

页码：387 / 392

页数：6

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