Analyzing peer-to-peer traffic across large networks
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Profiling internet backbone traffic: behavior models and applications
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Enhanced Skype traffic identification
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
GTVS: Boosting the Collection of Application Traffic Ground Truth
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
Revealing the Unknown ADSL Traffic Using Statistical Methods
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
Challenging statistical classification for operational usage: the ADSL case
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Internet application traffic classification using fixed IP-port
APNOMS'09 Proceedings of the 12th Asia-Pacific network operations and management conference on Management enabling the future internet for changing business and new computing services
Quantifying the accuracy of the ground truth associated with Internet traffic traces
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analysis of the impact of sampling on NetFlow traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
MINETRAC: mining flows for unsupervised analysis & semi-supervised classification
Proceedings of the 23rd International Teletraffic Congress
Uncovering relations between traffic classifiers and anomaly detectors via graph theory
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Automatic protocol signature generation framework for deep packet inspection
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Statistical traffic classification by boosting support vector machines
Proceedings of the 7th Latin American Networking Conference
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
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Detailed knowledge of the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Traffic classification aims at identifying the traffic mixture in the network. Several different classification approaches can be found in the literature. However, the validation of these methods is weak and ad hoc, because neither a reliable and widely accepted validation technique nor reference packet traces with well-defined content are available. In this paper, a novel validation method is proposed for characterizing the accuracy and completeness of traffic classification algorithms. The main advantages of the new method are that it is based on realistic traffic mixtures, and it enables a highly automated and reliable validation of traffic classification. As a proof-of-concept, it is examined how a state-of-the-art traffic classification method performs for the most common application types.