Internet Traffic Classification Based on Incremental Support Vector Machines
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Machine learning methods have been deployed widely in Internet traffic classification, which identify encrypted traffic and proprietary protocols effectively based on statistical features of traffic flows. Among these methods, support vector machines (SVMs) have attracted increasing attention as it achieves the state of art performance in traffic classification compared with other machine learning methods. However, traditional SVMs-based traffic classifier also has its limitations in real application: high training complexity and computation cost on both memory and CPU, which leads to the frequent and timely updating of traffic classifier being impractical. In this paper, incremental SVMs (ISVM) model is first introduced to reduce the high training cost of memory and CPU, and realize traffic classifier’s high-frequency and quick updates Besides, a modified version of ISVM model with attenuation factor, called AISVM, is further proposed to utilize valuable information in the previous training data sets. The experimental results have proved the effectiveness of ISVM and AISVM models in traffic classification.
KeywordsInternet traffic classification Incremental learning Support vector machines Attenuation factor
This work was partly financially supported through grants from the National Natural Science Foundation of China (No. 60903083 and 61502123), Scientific planning issues of education in Heilongjiang Province (No. GBC1211062), and the research fund for the program of new century excellent talents (No. 1155-ncet-008). The authors thank the anonymous reviewers for their helpful suggestions.
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