C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Ensembling neural networks: many could be better than all
Artificial Intelligence
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Small-time scaling behavior of Internet backbone traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
A measurement study of correlations of internet flow characteristics
Computer Networks: The International Journal of Computer and Telecommunications Networking
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
Byte me: a case for byte accuracy in traffic classification
Proceedings of the 3rd annual ACM workshop on Mining network data
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Most of the current network traffic classification approaches employ single classifier method with achieving lower accuracy under small training set. Different from high flow accuracy, byte accuracy, as an important metric for network traffic classification, is usually ignored by many researchers. To address these two problems, this paper proposes a novel classification algorithm. It combines ensemble learning with cost-sensitive learning, which enables the classification model to achieve high flow accuracy as well as byte accuracy. By evaluating our algorithm with the real 7-day traces collected at the edge of the campus network, the results show that it can averagely obtain flow accuracy of 94% as well as byte accuracy of 81%.