Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Unique classifier selection approach for bagging algorithm
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Composite lightweight traffic classification system for network management
International Journal of Network Management
Early classification of network traffic through multi-classification
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
Using a behaviour knowledge space approach for detecting unknown IP traffic flows
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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A fast and robust scheme that classifies Internet packets according to their application types is proposed in this work. The scheme is deployed at ISP for QoS provisioning, scalability and reliability. The proposed classification scheme consists of two steps: feature selection and classification. For feature selection, practical features are extracted using the modified multistage filter. By using the genetic algorithm (GA) and a variant of the wrapper method, we obtain two sets of features for comparison. As to classifiers, decision trees such as J48 and REPTree. The decision trees are trained with selected features from real traffics. The trained decision trees are compared with a classifier using the NBKE approach in terms of accuracy and robustness. It is demonstrated by simulation results that decision trees with features selected by GA gives the best performance. Finally, early classification with modified multistage filters is proposed to reduce collision errors for fast and robust performance.