Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines

1 October 2006

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 2 (21647143) , 534-539
https://doi.org/10.1109/isda.2006.253893

Abstract

Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portion of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. App-level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and app-level classification, which merely uses transport layer information. The method uses optimized support vector machines to perform large learning tasks, which is common in network traffic identification. The experimental results show that the proposed method has high efficiency and promising accuracy

Keywords

This publication has 10 references indexed in Scilit:

Is P2P dying or just hiding?
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005
Class-of-service mapping for QoS
Published by Association for Computing Machinery (ACM) ,2004
Transport layer identification of P2P traffic
Published by Association for Computing Machinery (ACM) ,2004
Accurate, scalable in-network identification of p2p traffic using application signatures
Published by Association for Computing Machinery (ACM) ,2004
Towards Peer-to-Peer Traffic Analysis Using Flows
Published by Springer Nature ,2003
New Support Vector Algorithms
Neural Computation, 2000
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery, 1998
Support-vector networks
Machine Learning, 1995
The Nature of Statistical Learning Theory
Published by Springer Nature ,1995
Pattern classification by convex analysis
Journal of the Optical Society of America A, 1991