Visible to the public Biblio

Filters: Author is Yang, Bowen  [Clear All Filters]
2020-09-08
Yang, Bowen, Chen, Xiang, Xie, Jinsen, Li, Sugang, Zhang, Yanyong, Yang, Jian.  2019.  Multicast Design for the MobilityFirst Future Internet Architecture. 2019 International Conference on Computing, Networking and Communications (ICNC). :88–93.
With the advent of fifth generation (5G) network and increasingly powerful mobile devices, people can conveniently obtain network resources wherever they are and whenever they want. However, the problem of mobility support in current network has not been adequately solved yet, especially in inter-domain mobile scenario, which leads to poor experience for mobile consumers. MobilityFirst is a clean slate future Internet architecture which adopts a clean separation between identity and network location. It provides new mechanisms to address the challenge of wireless access and mobility at scale. However, MobilityFirst lacks effective ways to deal with multicast service over mobile networks. In this paper, we design an efficient multicast mechanism based on MobilityFirst architecture and present the deployment in current network at scale. Furthermore, we propose a hierarchical multicast packet header with additional destinations to achieve low-cost dynamic multicast routing and provide solutions for both the multicast source and the multicast group members moving in intra- or inter-domain. Finally, we deploy a multicast prototype system to evaluate the performance of the proposed multicast mechanism.
2020-07-03
Yang, Bowen, Liu, Dong.  2019.  Research on Network Traffic Identification based on Machine Learning and Deep Packet Inspection. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1887—1891.

Accurate network traffic identification is an important basis for network traffic monitoring and data analysis, and is the key to improve the quality of user service. In this paper, through the analysis of two network traffic identification methods based on machine learning and deep packet inspection, a network traffic identification method based on machine learning and deep packet inspection is proposed. This method uses deep packet inspection technology to identify most network traffic, reduces the workload that needs to be identified by machine learning method, and deep packet inspection can identify specific application traffic, and improves the accuracy of identification. Machine learning method is used to assist in identifying network traffic with encryption and unknown features, which makes up for the disadvantage of deep packet inspection that can not identify new applications and encrypted traffic. Experiments show that this method can improve the identification rate of network traffic.