Cooperative Learning for Distributed In-Network Traffic Classification

S.B. Joseph, H.R. Loo, I. Ismail, T. Andromeda, M.N. Marsono
2016 Proceeding of the Electrical Engineering Computer Science and Informatics  
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
more » ... rform 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.
doi:10.11591/eecsi.v3i1.1144 fatcat:xaa2oacxbngcbj57btln6hqjmq