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
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
收藏
页码:24 / 30
页数:7
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