Efficient application identification and the temporal and spatial stability of classification schema
Computer Networks: The International Journal of Computer and Telecommunications Networking
GTVS: Boosting the Collection of Application Traffic Ground Truth
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
TIE: A Community-Oriented Traffic Classification Platform
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
On the stability of the information carried by traffic flow features at the packet level
ACM SIGCOMM Computer Communication Review
Per flow packet sampling for high-speed network monitoring
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
Traffic classification techniques supporting semantic networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
A framework for tunneled traffic analysis
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
A VoIP Traffic Identification Scheme Based on Host and Flow Behavior Analysis
Journal of Network and Systems Management
Improving matching performance of DPI traffic classifier
Proceedings of the 2011 ACM Symposium on Applied Computing
Network flow classification based on the rhythm of packets
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
Online NetFPGA decision tree statistical traffic classifier
Computer Communications
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Traffic classification is of fundamental importance to track the evolution of network applications and model their behaviours. Further, classified traffic is required to understand how the Internet is being used, and to effectively control the services that traffic receives. In this paper we present a machine-learning approach that accurately classifies live traffic using C4.5 decision tree. By collecting 12 features at the start of the flows, without inspecting the packet payload, our method can identify live traffic of different types of applications with 99.8% total accuracy. Moreover, accuracy is not our only concern; we also consider the latency and throughput as of high importance.