| Title |
Traffic Classification And Application Identification Based On Machine Learning In Large-Scale Supercomputing Center |
| ID_Doc |
58530 |
| Authors |
Zhao S.; Ye K.; Xu C.-Z. |
| Year |
2019 |
| Published |
Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 |
| DOI |
http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00319 |
| Abstract |
Internet traffic classification and application identification associated with network traffic is an essential step for network security and traffic engineering. The traditional port-based, payload-based or statistic-based classification methods do not work well in practice. To solve the problem, in this paper, we propose a new classification model which is verified by supervised learning algorithms (e.g., RandomForest, C4.5, KNN). The model includes two main parts: a clustering flow label propagation technique based on equivalent flow-labeled propagation and a synthetic-flow feature generation algorithm based on Bidirectional-flow (BDF). To further improve the accuracy of traffic classifiers and reduce the cost, we also propose a feature selection algorithm. The experiments are done on real-world network traffic from a large-scale Supercomputing center, showing that the proposed model achieves better performance and higher accuracy for network traffic classification. © 2019 IEEE. |
| Author Keywords |
application identification; machine learning; traffic classification |
