• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Communications and Intelligence Information Security >TCFOM: A robust traffic classification framework based on OC-SVM combined with MC-SVM
【24h】

TCFOM: A robust traffic classification framework based on OC-SVM combined with MC-SVM

机译：TCFOM：基于OC-SVM的强大流量分类框架与MC-SVM相结合

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

New application traffic occurring on Internet frequently challenges the traditional traffic classifiers based on machine learning. These classifiers always identify it inaccurately and assign it into one of their known classes forcibly, even though the extra class is labeled as 'other' when training. In this case, the precision of identifying known classes is reduced. In this paper, a robust traffic classification framework based on OC-SVM combined with MC-SVM (TCFOM) is presented. We capture several kinds of application traffic, and carry out an experiment under supervised environment. Using the OC-SVM, the unknown traffic is classified into extra class labeled as 'other'. The precision of identifying known traffic is improved. Using the unknown traffic identified, the new classifying model is set up. TCFOM can classify the unknown traffic and extend well. We compare TCFOM with three classifiers respectively based on SVM, RBF network, Naive Bayes. Experimental results show that the robustness of TCFOM is best.
机译：基于机器学习，在互联网上发生的新应用流量经常挑战传统的交通分类器。这些分类器始终不准确地识别并强制将其分配到他们的一个已知类中，即使额外的类被标记为“其他”培训时。在这种情况下，减少了识别已知类的精度。本文介绍了一种基于OC-SVM与MC-SVM（TCFOM）组合的强大流量分类框架。我们捕获了几种应用程序流量，并在监督环境下进行实验。使用OC-SVM，未知流量被分类为标记为“其他”的额外类。识别已知流量的精度得到改善。使用所识别的未知流量，设置了新的分类模型。 TCFOM可以对未知流量进行分类并延展良好。我们将TCFOM与三个分类器相比，基于SVM，RBF网络，天真贝叶斯。实验结果表明，TCFOM的鲁棒性最佳。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号