Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

Authors:
Mehdi Mohammadi;Bijan Raahemi;Ahmad Akbari;Hossein Moeinzadeh;Babak Nasersharif
Affiliations:
Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran;Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran;Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran;Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran;Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Tehran 16846-13114, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.