A network traffic identification method based on finite state machine

Authors:
Bo Xu;Ming Chen;Changyou Xing;Guomin Zhang
Affiliations:
Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

Citing 9
Cited 0

Analyzing peer-to-peer traffic across large networks

Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Accurate, scalable in-network identification of p2p traffic using application signatures

Proceedings of the 13th international conference on World Wide Web
Transport layer identification of P2P traffic

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
The CoralReef Software Suite as a Tool for System and Network Administrators

LISA '01 Proceedings of the 15th USENIX conference on System administration
BLINC: multilevel traffic classification in the dark

Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic classification through simple statistical fingerprinting

ACM SIGCOMM Computer Communication Review
Early application identification

CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Toward the accurate identification of network applications

PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identifying network traffic accurately and efficiently is crucial for network management and security. Though several identification methods were proposed recently, none of them is shown to be better than all others and recognized as a de facto one. In this paper, an analysis of existing methods is given, and then a Perfect Protocol Finite State Machine (PPFSM) that can describe different identification methods is put forward. Based on these works, we proposed a traffic identification method named Network Flow Sequence Identification (NFSI), which utilizes the characteristics of PPFSM states transformation to identify network traffics. Finally, experiment results of identifying Skype voice traffic showed the accuracy and efficiency of NFSI.