A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Behavioral Authentication of Server Flows
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Characterization of network-wide anomalies in traffic flows
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Testing network-based intrusion detection signatures using mutant exploits
Proceedings of the 11th ACM conference on Computer and communications security
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
MSMiner-a developing platform for OLAP
Decision Support Systems
Expert Systems with Applications: An International Journal
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
A local-density based spatial clustering algorithm with noise
Information Systems
Introduction: Advances in intelligent information processing
Information Systems
Computer Methods and Programs in Biomedicine
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Business-to-business electronic market place selection
Enterprise Information Systems
Application controlled caching for web servers
Enterprise Information Systems
Electronic supply chain management applications by Swedish SMEs
Enterprise Information Systems
Flood decision support system on agent grid: method and implementation
Enterprise Information Systems
Electronic marketplace definition and classification: literature review and clarifications
Enterprise Information Systems
Advances in enterprise information systems
Information Systems Frontiers
Support vector machines for credit scoring and discovery of significant features
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Heuristic algorithms for effective broker deployment
Information Technology and Management
Research on e-Government evaluation model based on the principal component analysis
Information Technology and Management
Machine learning approach for IP-flow record anomaly detection
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Performance evaluation for a transportation system in stochastic case
Computers and Operations Research
Network traffic classification via HMM under the guidance of syntactic structure
Computer Networks: The International Journal of Computer and Telecommunications Networking
Relaxed constraints support vector machine
Expert Systems: The Journal of Knowledge Engineering
Toward an efficient and scalable feature selection approach for internet traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Online NetFPGA decision tree statistical traffic classifier
Computer Communications
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Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% is achieved when un-biased training and testing samples are used with the same feature set. Furthermore, as all the feature parameters are computable from the packet headers, the proposed method is also applicable to encrypted network traffic.