Research Article
Machine Learning Based Autonomous Network Flow Identifying Method
@INPROCEEDINGS{10.1007/978-3-642-30493-4_43, author={Hongbo Shi and Tomoki Hamagami and Haoyuan Xu}, title={Machine Learning Based Autonomous Network Flow Identifying Method}, proceedings={Wireless Internet. 6th International ICST Conference, WICON 2011, Xi’an, China, October 19-21, 2011, Revised Selected Papers}, proceedings_a={WICON}, year={2012}, month={10}, keywords={IPv6 SIP IP flow SOM GHSOM classification}, doi={10.1007/978-3-642-30493-4_43} }- Hongbo Shi
Tomoki Hamagami
Haoyuan Xu
Year: 2012
Machine Learning Based Autonomous Network Flow Identifying Method
WICON
Springer
DOI: 10.1007/978-3-642-30493-4_43
Abstract
Recently, various applications and services start to be used in the Internet. Load balancing the increasing network traffic in real time can affect the network quality. The flow control technologies become much more important than before. Our research project proposes an intelligent network flow identifying method, smart flow, which is based on the learning algorithm. In this paper, we suggest to utilize the SOM for learning the properties of packets, such as timestamp, source and destination. Based on our proposed normalization, IP network flows can be formed autonomously during the learning process. Furthermore, the combination use of the new normalization with the GHSOM can classify the sub-IP flows belongs to the same flow. This paper indicates that a flow shall consist of several sub-IP flows, and sub-IP flow shall consist of several IP packets.