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Title:
fast fourier transform based ip traffic classification system for sipto at h(e)nb
Author: Han Lin ; Huang Liusheng ; Hu Qian ; Han Xue ; Shi Jinglin
Source: 2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012 - Proceedings
Conference Name: 2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012
Conference Date: August 7, 2012 - August 10, 2012
Issued Date: 2012
Conference Place: Kun Ming, China
Keyword: Computer simulation ; Ethers ; Fast Fourier transforms ; Learning algorithms ; Mobile telecommunication systems
Indexed Type: EI
ISBN: 9781467326995
Department: (1) School of Software and Engineering University of Science and Technology of China Suzhou China; (2) Wireless Communication Technology Research Center Institute of Computing Technology Chinese Academy of Sciences China; (3) Beijing Key Laboratory of Mobile Computing and Pervasive Device China; (4) Beijing Sylincom Technologies Co. Ltd. China
Sponsorship: EAI; IEEE Computer Society
Abstract: 3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates. © 2012 IEEE.
English Abstract: 3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15945
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Han Lin,Huang Liusheng,Hu Qian,et al. fast fourier transform based ip traffic classification system for sipto at h(e)nb[C]. 见:2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012. Kun Ming, China. August 7, 2012 - August 10, 2012.
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