An Efficient Network Traffic Classification Based on Unknown and Anomaly Flow Detection Mechanism
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : G.Suganya|
|DOI : 10.14445/22312803/IJCTT-V10P132|
G.Suganya."An Efficient Network Traffic Classification Based on Unknown and Anomaly Flow Detection Mechanism". International Journal of Computer Trends and Technology (IJCTT) V10(4):187-191, Apr 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Traffic classification technique is an important tool for network and system security in the environments such as cloud computing based environment. Modern traffic classification methods plans to take the gain of flow statistical features and machine learning methods, but the classification performance is affected by reduced supervised information, and unfamiliar applications. In addition detection of anomalies in the flow level is not considered in earlier approaches. Current work proposes Flow-level anomaly detection with the framework of Unknown Flow Detection approaches. Flow-level anomaly can be detected by using Synthetic flow-level traffic trace generation approach(SG –FLT). The two major challenges with such an approach are to characterize normal and anomalous network behavior, and to discover realistic models defining normal and anomalous traffic at the flow level. Unknown flow detection approach has been performed by Flow level propagation and finding the correlated flows to boost the classification accuracy. Performance evaluation is conducted on real-world network traffic datasets which demonstrates that the proposed scheme provides efficient performance than existing methods in the complex network environment.
. T. Karagiannis, K. Papagiannaki, and M. Faloutsos, “BLINC: multileveltraffic classification in the dark,” SIGCOMM Comput. Commun. Rev.,vol. 35, pp. 229–240, Aug. 2005.
. Y. Xiang, W. Zhou, and M. Guo, “Flexible deterministic packet marking:an IP traceback system to find the real source of attacks,” IEEE Trans.Parallel Distrib. Syst., vol. 20, no. 4, pp. 567–580, Apr. 2009.
. M. Roughan, S. Sen, O. Spatscheck, and N. Duffield, “Class-of-servicemapping for QoS: a statistical signature-based approach to IP trafficclassification,” in Proc. 2004 ACM SIGCOMM Conference on InternetMeasurement, pp. 135–148.
. A. W. Moore and D. Zuev, “Internet traffic classification using Bayesiananalysis techniques,” SIGMETRICS Perform. Eval. Rev., vol. 33, pp. 50–60, June 2005
. A. McGregor, M. Hall, P. Lorier, and J. Brunskill, “Flow clusteringusing machine learning techniques,” in Proc. 2004 Passive and ActiveMeasurement Workshop, pp. 205–214.
. H. Kim, K. Claffy, M. Fomenkov, D. Barman, M. Faloutsos, and K. Lee,“Internet traffic classification demystified: myths, caveats, and the bestpractices,” in Proc. 2008 ACM CoNEXT Conference, pp. 1–12.
. Lorier, McGregor, M. Hall, P., and J. Brunskill, “Flow clusteringusing machine learning techniques,” in Proc. 2004 Passive and ActiveMeasurement Workshop, pp. 205–214.
. L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule, and K. Salamatian,“Traffic classification on the fly,” SIGCOMM Comput. Commun. Rev.,vol. 36, pp. 23–26, Apr. 2006.
. J. Erman, A. Mahanti, M. Arlitt, and C. Williamson, “Identifying anddiscriminating between web and peer-to-peer traffic in the network core,”in Proc. 2007 International Conference on World Wide Web, pp. 883–892.
Traffic classification, unknown flow detection, anomaly flow detection, compound classification