The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 53-515
Full Name: Kuldeep Singh
Position: Student
Age: ON
Sex: Male
Address: Kuldeep Singh S/o Jasdev Singh, V.P.O. Dhulkot, Distt. Ludhiana, Punjab, India- 141204
Country: INDIA
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E-mail address: kuldeepsinghbrar87@gmail.com
Other E-mails: kuldeepsinghbrar87@yahoo.com
Title of the Paper: A Near Real-time IP Traffic Classification using Machine Learning
Authors as they appear in the Paper: Kuldeep Singh, S. Agrawal
Email addresses of all the authors: kuldeepsinghbrar87@gmail.com, s.agrawal@hotmail.com
Number of paper pages: 10
Abstract: With drastic increase in internet traffic over last couple of years due to increase in number of internet users, IP traffic classification becomes very necessary task for various internet service providers for optimization of their network performance and for governmental intelligence organizations. Today, traditional IP traffic classification techniques such as port number and payload based direct packet inspection techniques are rarely used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for IP traffic classification. In this research paper, a real time internet traffic dataset has been developed using packet capturing tool for 2 second packet capturing duration and a second dataset is developed by reducing number of features of 2 second packet capture duration dataset. After that, fi!
ve ML algorithms Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBF), C4.5 Decision Tree, Bayes Net and Naïve Bayes are employed for IP traffic classification with these datasets. This experimental analysis shows that C4.5 and Bayes Net are effective ML techniques for IP traffic classification with reduction in packet capture duration and reduction in number of features used to characterize each application sample.
Keywords: IP traffic classification, Machine Learning, Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net, Naïve Bayes, Packet Capture Duration.
EXTENSION of the file: .doc
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How Did you learn about congress: IP Traffic Classification, Machine Learning Techniques
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