Efficient Algorithm Selection for Packet Classification using Machine Learning

被引：0

作者：

Elmahgiubi, Mohammed ^{[1
]}

Ahmed, Omar ^{[1
]}

Areibi, Shawki ^{[1
]}

Grewal, Gary ^{[2
]}

机构：

[1] Univ Guelph, Sch Engn, Guelph, ON, Canada

[2] Univ Guelph, Sch Comp Sci, Guelph, ON, Canada

来源：

2016 IEEE 21ST INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELLING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS (CAMAD) | 2016年

关键词：

Packet Classification; Algorithms Selection; Meta-Learning; Recommendation Systems; Artificial Neural Networks;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Many packet classification algorithms with variable performances and capabilities are available. However, no single algorithm is guaranteed to outperform every other one in every case. Meta-Learning is a subfield in Machine Learning that aims to apply statistical techniques to automate the algorithm selection process. In this work, we propose a novel framework for efficient, automatic packet classification algorithm selection. By utilizing Meta-Learning and Artificial Neural Networks (ANNs) we are able to achieve an average accuracy of 90% when automatically choosing the most appropriate algorithm when applied to over a hundred different rulesets ranging in size from 1K to 5K.

引用

页码：24 / 30

页数：7

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