Retraining Mechanism for On-Line Peer-to-Peer Traffic Classification
作者： Roozbeh Zarei , Alireza Monemi , Muhammad Nadzir Marsono
DOI: 10.1007/978-3-642-32063-7_40
关键词:
摘要: Peer-to-Peer (P2P) detection using machine learning (ML) classification is affected by its training quality and recency. In this paper, a practical retraining mechanism proposed to retrain an on-line P2P ML classifier with the changes in network traffic behavior. This evaluates accuracy of based on datasets containing flows labeled heuristic dataset generator. The retrained if falls below predefined threshold. system has been evaluated traces captured from Universiti Teknologi Malaysia (UTM) campus between October November 2011. overall results shows that generation can generate accurate classifying high (98.47%) low false positive (1.37%). which built J48 algorithm demonstrated be capable self-retraining over time.
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