Applied multivariate statistical analysis
Applied multivariate statistical analysis
Empirically derived analytic models of wide-area TCP connections
IEEE/ACM Transactions on Networking (TON)
Analyzing peer-to-peer traffic across large networks
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
An analysis of Internet content delivery systems
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
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
The CoralReef Software Suite as a Tool for System and Network Administrators
LISA '01 Proceedings of the 15th USENIX conference on System administration
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Data Mining
On Pairwise Naive Bayes Classifiers
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient application identification and the temporal and spatial stability of classification schema
Computer Networks: The International Journal of Computer and Telecommunications Networking
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
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Traffic classification by Internet applications, even on off-line mode, can be interesting for many applications such as attack identification, QoS prioritization, network capacity planning and also computer forensic tools. Into the classification problem context is well-known the fact that a higher number of discriminators not necessarily will increase the discrimination power. This work investigates a methodology for features selection and Internet traffic classification in which the problem to classify one among M classes is split in M one-against-all binary classification problems, with each binary problem adopting eventually a set of different discriminators. Different combinations of discriminators selection methods, classification methods and decision algorithms could be embedded into the methodology. To investigate the performance of this methodology we have used the Naïve Bayes classifier to select the set of discriminators and for classification. The proposed method intends to reduce the total number of different discriminators used into the classification problem. The methodology was tested for classification of traffic flows and the experimental results showed that we can reduce significantly the number of discriminators per class sustaining the same accuracy level.