Elements of information theory
Elements of information theory
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Lightweight application classification for network management
Proceedings of the 2007 SIGCOMM workshop on Internet network management
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
A Machine Learning Approach for Efficient Traffic Classification
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Gradient-based manipulation of nonparametric entropy estimates
IEEE Transactions on Neural Networks
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
Internet traffic classification demystified: on the sources of the discriminative power
Proceedings of the 6th International COnference
Quantifying the accuracy of the ground truth associated with Internet traffic traces
Computer Networks: The International Journal of Computer and Telecommunications Networking
Graption: A graph-based P2P traffic classification framework for the internet backbone
Computer Networks: The International Journal of Computer and Telecommunications Networking
Padding and fragmentation for masking packet length statistics
TMA'12 Proceedings of the 4th international conference on Traffic Monitoring and Analysis
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This paper presents a statistical analysis of the amount of information that the features of traffic flows observed at the packet-level carry, with respect to the protocol that generated them. We show that the amount of information of the majority of such features remain constant irrespective of the point of observation (Internet core vs. Internet edge) and to the capture time (year 2000/01 vs. year 2008). We also describe a comparative analysis of how four statistical classifiers fare using the features we studied.