Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Redundant feature elimination for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
ACM SIGCOMM Computer Communication Review
Efficient application identification and the temporal and spatial stability of classification schema
Computer Networks: The International Journal of Computer and Telecommunications Networking
Profiling and identification of P2P traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Traffic classification using a statistical approach
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
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P2P applications supposedly constitute a substantial proportion of today's Internet traffic. The ability to accurately identify different P2P applications in internet traffic is important to a broad range of network operations including application-specific traffic engineering, capacity planning, resource provisioning, service differentiation, etc. In this paper, we present a Neural Network approach that precisely identifies the P2P traffic using Multi-Layer Perceptron (MLP) neural network. It is general practice to reduce the cost of classification by reducing the number of features, utilizing some feature selection algorithm. The reduced feature set produced by such algorithms are highly data-dependent and are different for different data sets. Further the feature set produced from one data set does not yield good results when tried upon other data sets. We propose an optimum and universal set of features which is independent of training and test data sets. The proposed feature set has enabled us to achieve significant improvement in performance of the MLP classifier. The few features in the proposed feature set results in a significant reduction in training time, while maintaining the performance, thereby making this approach suitable for real-time implementation.