Cooperative Learning for Distributed In-Network Traffic Classification

IAES International Conference on Electrical Engineering, Computer Science and Informatics

电工学;计算机科学

Joseph, S.B.^1 ; Loo, H.R.^1 ; Ismail, I.^1 ; Andromeda, T.^2 ; Marsono, M.N.^1

Department of Electronics and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru

81310, Malaysia^1

Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia^2

关键词: Cooperative learning;    Distributed networks;    In networks;    Information exchanges;    Network information;    Network node;    On-line traffic;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/190/1/012010/pdf
DOI  :  10.1088/1757-899X/190/1/012010

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.