The nature of statistical learning theory
The nature of statistical learning theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
HMM profiles for network traffic classification
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
The Journal of Machine Learning Research
Semi-supervised network traffic classification
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic application-layer protocol analysis for network intrusion detection
USENIX-SS'06 Proceedings of the 15th conference on USENIX Security Symposium - Volume 15
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Identify P2P Traffic by Inspecting Data Transfer Behaviour
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
An SVM-based machine learning method for accurate internet traffic classification
Information Systems Frontiers
TCP traffic classification using markov models
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
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Network traffic classification is the basis of many network technologies including intrusion detection, traffic scheduling, and quality of service. Given the limitations of existing classification approaches based on the port number, the packet-payload and statistical characteristics of network traffic, in this paper we propose a novel classification method via a hidden Markov model. With the analysis about the time series characteristics and statistical properties of network traffic, we use a hidden Markov model to model for a type of traffic under the guidance of syntactic structure of it. And then a classification approach is presented based on the model. Experiment results on several typical network applications indicate that the combination of time series characteristics and the statistical properties not only make the established model more precise, but also improve the accuracy of network traffic classification.