An introduction to variable and feature selection
The Journal of Machine Learning Research
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
Flexible Deterministic Packet Marking: An IP Traceback System to Find the Real Source of Attacks
IEEE Transactions on Parallel and Distributed Systems
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
Detailed analysis of Skype traffic
IEEE Transactions on Multimedia
Content based image retrieval using unclean positive examples
IEEE Transactions on Image Processing
Early recognition of encrypted applications
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis
IEEE/ACM Transactions on Networking (TON)
KISS: stochastic packet inspection classifier for UDP traffic
IEEE/ACM Transactions on Networking (TON)
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
Classifying internet one-way traffic
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
Network Traffic Classification Using Correlation Information
IEEE Transactions on Parallel and Distributed Systems
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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Traffic classification is a fundamental component in advanced network management and security. Recent research has achieved certain success in the application of machine learning techniques into flow statistical feature based approach. However, most of flow statistical feature based methods classify traffic based on the assumption that all traffic flows are generated by the known applications. Considering the pervasive unknown applications in the real world environment, this assumption does not hold. In this paper, we cast unknown applications as a specific classification problem with insufficient negative training data and address it by proposing a binary classifier based framework. An iterative method is proposed to extract unknown information from a set of unlabelled traffic flows, which combines asymmetric bagging and flow correlation to guarantee the purity of extracted negatives. A binary classifier is used as an application signature which can operate on a bag of correlated flows instead of individual flows to further improve its effectiveness. We carry out a series of experiments in a real-world network traffic dataset to evaluate the proposed methods. The results show that the proposed method significantly outperforms the-state-of-art traffic classification methods under the situation of unknown applications present.