Abstract
Machine learning approaches based on decision trees (DTs) have been proposed for classifying networking traffic. Although this technique has been proven to have the ability to classify encrypted and unknown traffic, the software implementation of DT cannot cope with the current speed of packet traffic. In this paper, hardware architecture of decision tree is proposed on NetFPGA platform. The proposed architecture is fully parameterizable to cover wide range of applications. Several optimizations have been done on the DT structure to improve the tree search performance and to lower the hardware cost. The optimizations proposed are: a) node merging to reduce the computation latency, b) limit the number of nodes in the same level to control the memory usage, and c) support variable throughput to reduce the hardware cost of the tree.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bermak, A., Martinez, D.: A compact 3D VLSI classifier using bagging threshold network ensembles. IEEE Transactions on Neural Networks 14, 1097–1109 (2003)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Eklund, P., Kirkby, S.: Machine learning classifier performance as an indicator for data acquisition regimes in geographical field surveys. In: Proceedings of the Third Australian and New Zealand Conference on Intelligent Information Systems, pp. 264–269 (1995)
Erman, J., Mahanti, A., Arlitt, M., Cohen, I., Williamson, C.: Offline/realtime traffic classification using semi-supervised learning. Performance Evaluation 64(9-12), 1194–1213 (2007)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth, Monterey (1984)
Lockwood, J.W., McKeown, N., Watson, G., Gibb, G., Hartke, P., Naous, J., Raghuraman, R., Luo, J.: NetFPGA–An Open Platform for Gigabit-Rate Network Switching and Routing. In: Proceedings of 2007 IEEE International Conference on Microelectronic Systems Education (2007)
Lopez-Estrada, S., Cumplido, R.: Decision Tree Based FPGA-Architecture for Texture Sea State Classification. In: IEEE International Conference on Reconfigurable Computing and FPGA’s (2006)
Moore, A.W., Papagiannaki, K.: Toward the Accurate Identification of Network Applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005)
NetFPGA (2012), http://www.netfpga.org/
Nguyen, T., Armitage, G.: Training on multiple sub-flows to optimise the use of machine learning classifiers in real-world IP networks. In: Proceedings of IEEE 31st Conference on Local Computer Networks, pp. 369–376 (2006)
Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys Tutorials 10(4), 56–76 (2008)
Qi, Y., Fong, J., Jiang, W., Xu, B., Li, J., Prasanna, V.: Multi-dimensional packet classification on FPGA: 100 Gbps and beyond. In: International Conference on Field-Programmable Technology, FPT, pp. 241–248 (2010)
Struharik, R., Novak, L.: Intellectual property core implementation of decision trees. IET, Computers Digital Techniques 3(3), 259–269 (2009)
Wang, Y., Yu, S.Z.: Machine Learned Real-time Traffic Classifiers. In: Second International Symposium on Intelligent Information Technology Application, vol. 3, pp. 449–454 (2008)
Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. Special Interest Group on Data Communication (SIGCOMM) 36(5), 5–16 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Monemi, A., Zarei, R., Marsono, M.N., Khalil-Hani, M. (2013). Parameterizable Decision Tree Classifier on NetFPGA. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-32063-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
eBook Packages: EngineeringEngineering (R0)