Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Polygraph: Automatically Generating Signatures for Polymorphic Worms
SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Unexpected means of protocol inference
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Support Vector Machines for TCP traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Early recognition of encrypted applications
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
KISS: stochastic packet inspection classifier for UDP traffic
IEEE/ACM Transactions on Networking (TON)
Mining unclassified traffic using automatic clustering techniques
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
Network Traffic Classification Using Correlation Information
IEEE Transactions on Parallel and Distributed Systems
Foreword: Special issue of JCSS on UbiSafe computing and communications
Journal of Computer and System Sciences
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In network traffic classification, ''unknown applications'' is a difficult problem unsolved. Conventional supervised classification methods classify any traffic flow into predefined classes, while cannot handle unknown applications without corresponding supervised data. Some unsupervised clustering algorithms, such as k-means, have been applied to group traffic flows automatically, but a large number of resulting clusters are unable to correctly represent a small number of real applications. To address the problem of unknown applications, we propose a novel unsupervised approach which has the capability to discover application-based traffic classes and classify traffic flows according to their generation applications. In the proposed approach, flow statistical properties and IP packet payload are used in combination to discover traffic classes in the training stage. We introduce a bag-of-words (BoW) model to represent the content of clusters constructed by using flow statistical features, and apply the latent semantic analysis (LSA) to aggregate similar traffic clusters based on their payload content. In the testing stage, only flow statistical features are used to classify traffic flows, that can protect user privacy and deal with known encrypted applications without inspecting IP packets. A number of experiments are carried out on a real-world traffic dataset to demonstrate the effectiveness and robustness of the proposed approach.