C4.5: programs for machine learning
C4.5: programs for machine learning
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
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
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
A Preliminary Investigation of Skype Traffic Classification Using a Minimalist Feature Set
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Acceleration of decision tree searching for IP traffic classification
Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Machine learning based encrypted traffic classification: identifying SSH and skype
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Internet traffic classification demystified: on the sources of the discriminative power
Proceedings of the 6th International COnference
Real-Time Classification of Multimedia Traffic Using FPGA
FPL '10 Proceedings of the 2010 International Conference on Field Programmable Logic and Applications
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
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Machine learning (ML) algorithms have been shown to be effective in classifying the dynamic internet traffic today. Using additional features and sophisticated ML techniques can improve accuracy and can classify a broad range of application classes. Realizing such classifiers to meet high data rates is challenging. In this paper, we propose two architectures to realize complete online traffic classifier using flow-level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL discretization algorithm. It achieves an accuracy of 97.92% when classifying a traffic trace consisting of eight application classes. Next, we accelerate our classifier using two architectures on FPGA. One architecture stores the classifier in on-chip distributed RAM. It is designed to sustain a high throughput. The other architecture stores the classifier in block RAM. It is designed to operate with small hardware footprint and thus built at low hardware cost. Experimental results show that our high throughput architecture can sustain a throughput of $550$ Gbps assuming 40 Byte packet size. Our low cost architecture demonstrates a 22% better resource efficiency than the high throughput design. It can be easily replicated to achieve $449$ Gbps while supporting 160 input traffic streams concurrently. Both architectures are parameterizable and programmable to support any binary-tree-based traffic classifier. We develop a tool which allows users to easily map a binary-tree-based classifier to hardware. The tool takes a classifier as input and automatically generates the Verilog code for the corresponding hardware architecture.