Averaging over decision stumps
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A maximum entropy approach to species distribution modeling
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Unexpected means of protocol inference
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Lightweight application classification for network management
Proceedings of the 2007 SIGCOMM workshop on Internet network management
Improving the Analysis of Lawfully Intercepted Network Packet Data Captured for Forensic Analysis
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Pattern Recognition Approaches for Classifying IP Flows
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
A biologically inspired QoS routing algorithm for mobile ad hoc networks
International Journal of Wireless and Mobile Computing
Tracking long duration flows in network traffic
INFOCOM'10 Proceedings of the 29th conference on Information communications
Experience with high-speed automated application-identification for network-management
Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
QoS routing protocols for mobile ad hoc networks: a survey
International Journal of Wireless and Mobile Computing
Feature selection for optimizing traffic classification
Computer Communications
Wireless Personal Communications: An International Journal
A dynamic QoS provisioning call admission control in cellular mobile using fuzzy logic
International Journal of Wireless and Mobile Computing
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Traffic classification has wide applications in network measurements, network security and quality of service. Recent research tends to apply machine learning methods based on flow statistical features to improve traffic classification performance. In this paper, we propose a novel non-parametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. Meanwhile, our system employs a lightweight modular architecture, which combines a series of simple linear binary classifiers, each of which can be efficiently implemented and trained on vast amounts of flow data in parallel, to achieve scalability while attaining high accuracy. A large number of experiments are carried out on real traffic data to validate the proposed approach. The results show that the traffic classification performance can be improved significantly while meeting the scalability and stability requirements of large networks.