@inproceedings{069858a432ad4814af602612a2f51056,
title = "Network traffic classification with improved random forest",
abstract = "Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance of the corresponding variable to classify network traffic. Our test results show that the Improved Random Forest has better classification performance. And it takes less time to build the model.",
keywords = "Random forest, Selection probability, Traffic classification",
author = "Chao Wang and Tongge Xu and Xi Qin",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th International Conference on Computational Intelligence and Security, CIS 2015 ; Conference date: 19-12-2015 Through 20-12-2015",
year = "2016",
month = feb,
day = "1",
doi = "10.1109/CIS.2015.27",
language = "英语",
series = "Proceedings - 2015 11th International Conference on Computational Intelligence and Security, CIS 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--81",
booktitle = "Proceedings - 2015 11th International Conference on Computational Intelligence and Security, CIS 2015",
address = "美国",
}