Network Traffic Classification Based on Relevant Vector Machine

Relevant vector machine (RVM) is applied in network traffic classification. Firstly, experiment data is standardized, and then RVM is compared with other machine learning tools. Lastly, doubting interval is introduced to analyze predicted probability of classification, based on which a new hybrid traffic classification approach is proposed. Experiment studies illustrate that: 1) RVM excels the support vector machine (SVM) in three performances, and moreover, its classification accuracy is rather high in the situation of small sample circumstances; 2) probabilistic classification in doubting interval has a rather low classification accuracy while an accuracy above 98% outside doubting interval.