Title :
P2P traffic identification based on bayesian regularization BP neural network
Author :
Jingquan, Zhou ; Yanxia, Li ; Zhenzhen, Cai ; Juan, Li ; Linkai, Zhou ; Jiebao, Cao
Author_Institution :
Coll. of Electron. Sci. & Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
It will increase identification accuracy when P2P traffic identification method based on flow feature is combined with machine learning methods. Recently, the most applied machine learning method is neural networks, but neural networks has insufficient generalization ability, this paper proposes an identification method based on BP neural network that use bayesian regularization to improve its generalization ability. The simulation results show that this method can effectively improve the identification accuracy in practice.
Keywords :
backpropagation; belief networks; generalisation (artificial intelligence); learning (artificial intelligence); neural nets; peer-to-peer computing; telecommunication traffic; Bayesian regularization BP neural network; P2P traffic identification accuracy; flow feature; insufficient generalization ability; machine learning methods; Training; P2P traffic identification; bayesian regularization; generalization;
Conference_Titel :
Communication Technology (ICCT), 2010 12th IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-6868-3
DOI :
10.1109/ICCT.2010.5688839