Synopsis
With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum.
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About the Author
Kuldeep Singh received B. Tech degree in Electronics & Communication Engineering in 2009 from Punjab Technical University, Jalandhar, Punjab, India and M.E. degree in Electronics & Communication Engineering from Panjab University, Chandigarh, India. He has done his research work under the supervision of Mr. Sunil Agrawal, PU, Chandigarh, India.
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Internet Traffic Classification
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum. 100 pp. Englisch. Seller Inventory # 9783846595619
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh KuldeepKuldeep Singh received B. Tech degree in Electronics & Communication Engineering in 2009 from Punjab Technical University, Jalandhar, Punjab, India and M.E. degree in Electronics & Communication Engineering from Panjab U. Seller Inventory # 5502064
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Internet Traffic Classification
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. Seller Inventory # 9783846595619
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Internet Traffic Classification : A Machine Learning Approach
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum. Seller Inventory # 9783846595619