Publication Type:

Conference Paper

Source:

2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) (2017)

Keywords:

Algorithm design and analysis, Bayes Classifier, Bayes method, Bayes methods, Classification algorithms, Comparative performance analysis, Computer science, decorate algorithm, Ensemble learning, ensemble learning methods, Internet, learning (artificial intelligence), Network traffic classification, Pattern classification, Ports (Computers), subset evaluation feature selection method, supervised learning algorithms, telecommunication computing, Telecommunication traffic, Tree classifiers, Trees (mathematics), wrapper method-classifier

Abstract:

Network traffic classification has become vital due to the increased flow of web traffic from services like HTTP, FTP, and SMTP etc. The idea of Network traffic classification is to categorize the network traffic and regulate the flow of data in the network there by monitoring the traffic. In this paper, a comparative analysis of different supervised learning algorithms like Ensemble learning methods, Trees, Bayes method are done and results are examined. To select the most reliable features, wrapper method-classifier subset evaluation feature selection method is used. From the analysis results of different algorithms, decorate algorithm which belongs to the ensemble classifiers performs better than other algorithms.

Notes:

cited By 0; Conference of 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017 ; Conference Date: 6 January 2017 Through 7 January 2017; Conference Code:130103

Cite this Research Publication

Archanaa R., Athulya, V., Rajasundari, T., and M.V.K. Kiran, “A comparative performance analysis on network traffic classification using supervised learning algorithms”, in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017.

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