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
The devil and packet trace anonymization
ACM SIGCOMM Computer Communication Review
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
The Journal of Machine Learning Research
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
Automatic discovery of botnet communities on large-scale communication networks
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
BotCop: An Online Botnet Traffic Classifier
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
TIE: A Community-Oriented Traffic Classification Platform
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
Identify P2P Traffic by Inspecting Data Transfer Behaviour
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Identify P2P traffic by inspecting data transfer behavior
Computer Communications
Hybrid traffic classification approach based on decision tree
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Internet traffic classification demystified: on the sources of the discriminative power
Proceedings of the 6th International COnference
NeTraMark: a network traffic classification benchmark
ACM SIGCOMM Computer Communication Review
Clustering botnet communication traffic based on n-gram feature selection
Computer Communications
Feature extraction based IP traffic classification using machine learning
ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
Uncovering relations between traffic classifiers and anomaly detectors via graph theory
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Kiss to abacus: a comparison of P2P-TV traffic classifiers
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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Many reputable research groups have published several interesting papers on traffic classification, proposing mechanisms of different nature. However, it is our opinion that this community should now find an objective and scientific way of comparing results coming out of different groups. We see at least two hurdles before this can happen. A major issue is that we need to find ways to share full-payload data sets, or, if that does not prove to be feasible, at least anonymized traces with complete application layer meta-data. A relatively minor issue refers to finding an agreement on which metric should be used to evaluate the performance of the classifiers. In this note we argue that these are two important issues that the community should address, and sketch a few solutions to foster the discussion on these topics.