Xiang, Yang 2013, Classifying network traffic in the big data era, in INCOS 2013 : Proceedings of the 5th International Conference on Intelligent Networking and Collaborative Systems, IEEE Computer Society, Piscataway, N.J., pp. xxxi-xxxi, doi: 10.1109/INCoS.2013.9.
International Conference on Intelligent Networking and Collaborative Systems
IEEE Computer Society
Place of publication
With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.
Field of Research
080503 Networking and Communications 080501 Distributed and Grid Systems
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact email@example.com.