Internet Traffic Classification Using Multifractal Analysis Approach

In this paper, we present a traffic classifier based on the theory of multifractal network traffic. We use precisely the concept of multiplicative binomial cascades to get a feature vector to be used in the classification scheme. This vector is obtained by the multiplier variances of the multiplicative cascade traffic view. We analyze the performance of the technique proposed by a popular ML Software-based and the results showed viability classification rates of traffic over 90%.443 BOOK3842Society for Modeling and Simulation International (SCS)(2011) Global Internet Traffic Projected to Quadruple by 2015, , http://newsroom.cisco.com/dlls/2011/prod_060111.html, last accessed June 2011. AvailableAlshammari, R., Zincir-Heywood, A.N., A Flow Based Approach for SSH Traffic Detection (2007) Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, 2007, pp. 296-301Hurley, J., Garcia-Palacios, E., Sezer, S., Classifying Network Protocols: A 'Two-Way' Flow Approach (2011) Communications, IET, 5, pp. 79-89Nguyen, T.T.T., Armitage, G., A Survey of Techniques for Internet Traffic Classification Using Machine Learning (2008) Communications Surveys & Tutorials, 10, pp. 56-76. , IEEEMoore, A.W., Zuev, D., Internet traffic classification using bayesian analysis techniques (2005) Performance Evaluation Review, 33 (1), pp. 50-60. , DOI 10.1145/1064212.1064220, SIGMETRICS 2005: International Conference on Measurement and Modeling of Computer Systems - ProceedingsErman, J., Mahanti, A., Arlitt, M., Internet Traffic Identification Using Machine Learning (2006) Global Telecommunications Conference, 2006. GLOBECOM '06, pp. 1-6. , IEEE, 2006Karagiannis, T., Broido, A., Faloutsos, M., Claffy, K., Transport Layer Identication of P2P Trfc (2004) Proc. of IMC'04Sen, S., Spatscheck, O., Wangccurte, D., Scalable In-Network Identication P2P Trfc Using Application Signatures (2004) WWW200S, New York, USA, May 17-22Moore, A., Zuevd, D., Crogan, M., (2005) Discriminators for Use in Flow-Based Classification, , technical report, intel researchZuev, D., Moore, A., Traffic Classification Using a Statistical Approach (2005) Lect.notescomput.sci., 3431, pp. 321-324Junior, G.P.S., Maia, J.E.B., Holanda, R., Sousa, J.N., P2P Traffic Identification Using Cluster Analysis (2007) Global Information Infrastructure Symp., Giis 2007, pp. 128-133Zander, S., Hguyen, T., Armitage, G., Automated Traffic Classification and Application Identification Using Machine Learning (2005) Proc. IEEE Conf. on Local Computer Networks 30th Anniversary, Lcn.Roughan, M., Sen, S., Spatscheck, O., Duffield, N., Class-of-Service Mapping for QoS: A Statistical Signature-Based Approach to IP Traffic Classification (2004) Proc. Fourth ACM Sigcomm Conf. on Internet Measurement, pp. 135-148Mcgregor, A., Hall, M., Lorier, P., Brunskill, J., Flow Clustering Using Machine Learning Techniques (2004) Proc. Fifth Passive and Active Measurement Workshop, PamLeland, W., Taqqu, M., Willinger, W., Wilson, D., On The Self-Similar Nature of Ethernet Traffic (1994) IEEE/ACM Transactions on Networking, 2 (1), pp. 1-15. , extended versionRiedi, R.H., Véhel, J.L., (1997) Multifractal Properties of TCP Traffic: A Numerical Study, , Technical Report 3129, INRIA RocquencourtFeldmann, A., Gilbert, A., Willinger, W., Kurtz, T.G., The Changing Nature of Network Traffic: Scaling Phenomena (1998) ACM Computer Communication Review, 28, pp. 5-29. , Group, Tech.1 Rep.Disas-STP-93-30Krishna, P.M., Gadre, V.M., Desai, U.B., Modelling and Control of Broadband Traffic Using Multiplicative Multifractal Cascades (2002) Sadhana, 27, pp. 699-723Kolmogorov, A.N., A Refinement of Previous Hypothses Concerning The Local Structure of Turbulence in a Viscous Incompressible Fluid a High Reynolds Number (1962) J.Fluid Mech., 13, pp. 82-85Mandelbrot, B.B., Calvet, L., Fisher, A., (1997) Large Deviations and the Distribution of Price Changes, , Discussion paper No 1165 of the Cowles Foundation for Economics at Yale UniversityGupta, V.K., Waymire, E.C., A statistical analysis of mesoscale rainfall as a random cascade (1993) Journal of Applied Meteorology, 32 (2), pp. 251-267Bickel, D.R., West, B.J., Multiplicative and fractal processes in DNA evolution (1998) Fractals, 6 (3), pp. 211-217Dang, T.D., Molnár, S., Maricza, I., Queuing Performance Estimation for General Multifractal Traffic (2003) Int. J. Commun. Syst., 16 (2), pp. 117-136Gilbert, A.C., Willinger, W., Feldmann, A., Scaling Analysis of Conservative Cascades, with Applications to Network Traffic (1999) Information Theory, IEEE Transactions on, 45, pp. 971-991. , AT&T Labs.-Res., Florham Park, NJQuinlan, J.R., (1993) C4.5: Programs for Machine Learning, , Morgan Kaufmann. Publishers Inc. San Francisco, CA, USANetwork Tools and Traffic Traces, , http://www.grid.unina.it/Traffic/Traces/ttraces.php, last accessed June 2011Carela-Español, V., Barlet-Ros, P., Aparicio, A.C., Pareta, S., Analysis of The Impact of Sampling on NetFlow Traffic Classification (2001) Computer Networks, 55, pp. 1083-1099Holmes, G., Donkin, A., Witten, H., WEKA: A Machine Learning Workbench (2012) Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on, Brisbane, Qld., Australia, pp. 357-361Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A., A Detailed Analysis of the KDD CUP 99 Data Set (2009) Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), , Submitted t