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Optimizing the Performance of Machine Learning Based Traffic Classification

Traffic classification is a critical technology in the areas of network management and security monitoring. Traditional port-based and payload-based classification are no longer effective due to the fact that many applications utilize unpredictable port numbers and packet encryption. Researchers tend to apply machine learning (ML) techniques to identify the traffic flows by recognizing statistical features. Unfortunately, looking back upon the related work, most of the ML-based classification algorithms have similar performance, and what really matters now is how to optimize these techniques. In this paper, we analyzed two critical issues (Feature Selection, Configuration of Parameters) of ML classification, and presented the corresponding viable methods to optimize the classification model. This paper also reported the experimental evaluation to assess the performance improvements introduced by our optimized methods; experimental results on real-life datasets and network traffic show that the classification model successfully achieves significant accuracy improvement.