CFP last date
20 June 2024
Reseach Article

HTTP Traffic Graph Clustering using Markov Clustering Algorithm

by Yessica Nataliani, Theophilus Wellem
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 2
Year of Publication: 2014
Authors: Yessica Nataliani, Theophilus Wellem
10.5120/15549-4344

Yessica Nataliani, Theophilus Wellem . HTTP Traffic Graph Clustering using Markov Clustering Algorithm. International Journal of Computer Applications. 90, 2 ( March 2014), 37-41. DOI=10.5120/15549-4344

@article{ 10.5120/15549-4344,
author = { Yessica Nataliani, Theophilus Wellem },
title = { HTTP Traffic Graph Clustering using Markov Clustering Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 2 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number2/15549-4344/ },
doi = { 10.5120/15549-4344 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:03.981397+05:30
%A Yessica Nataliani
%A Theophilus Wellem
%T HTTP Traffic Graph Clustering using Markov Clustering Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 2
%P 37-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Graph-based techniques and analysis have been used for IP network traffic analysis. The objective of this paper is to study the hosts' interaction behavior and use graph clustering algorithm, the Markov clustering algorithm, to group (cluster) hosts which have interaction using the HTTP protocol. Using real network traces, the clustering results show that MCL algorithm successfully group the hosts to their corresponding clusters. Analyzing the clustering results, it is showed that communications between one source IP address to one destination IP address, one source IP address to several (different) destination IP addresses, and several (different) source IP addresses to one destination IP address, are grouped to their own clusters.

References
  1. M. Iliofotou, "Exploring graph-based network traffic monitoring," in IEEE INFOCOM Workshops 2009, pp. 1-2, 2009.
  2. Y. Jin, E. Sharafuddin, and Z. -L. Zhang, "Unveiling core network-wide communication patterns through application traffic activity graph decomposition," in Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems, SIGMETRICS '09, (New York, NY, USA), pp. 49-60, ACM, 2009.
  3. M. Iliofotou, P. Pappu, M. Faloutsos, M. Mitzenmacher, S. Singh, and G. Varghese, "Network monitoring using tra_c dispersion graphs (TDGs)," in Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC '07, (New York, NY, USA), pp. 315-320, ACM, 2007.
  4. D. Q. Le, T. Jeong, H. E. Roman, and J. W. -K. Hong, "Traffic dispersion graph based anomaly detection," in Proceedings of the Second Symposium on Information and Communication Technology, SoICT '11, (New York, NY, USA), pp. 36-41, ACM, 2011.
  5. K. Xu, F. Wang, and L. Gu, "Behavior analysis of internet traffic via bipartite graphs and one-mode projections," IEEE/ACM Transactions on Networking, vol. Early Access Online, pp. 1-12, 2013.
  6. M. Iliofotou, H. -c. Kim, M. Faloutsos, M. Mitzenmacher, P. Pappu, and G. Varghese, 'Graption: A graph-based P2P traffic classification framework for the internet backbone," Comput. Netw. , vol. 55, pp. 1909-1920, June 2011.
  7. D. Q. Le, T. Jeong, H. Roman, and J. Hong, "Communication patterns based detection of anomalous network traffic," in 2012 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 185-185, 2012.
  8. Z. Mingqiang, H. Hui, and W. Qian, "A graph-based clustering algorithm for anomaly intrusion detection," in 2012 7th International Conference on Computer Science Education (ICCSE), pp. 1311-1314, 2012.
  9. K. Xu and F. Wang, "Behavioral graph analysis of internet applications," in 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1-5, Dec. 2011.
  10. Y. Jin, N. Du_eld, P. Ha_ner, S. Sen, and Z. -L. Zhang, "Can't see forest through the trees? understanding mixed traffic graphs from application class distribution," in Proceedings of 9th Workshop on Mining and Learning with Graphs MLG'11, pp. 1-8, 2011.
  11. Y. Dong and Y. Zhuang, "Fuzzy hierarchical clustering algorithm facing large databases," in Fifth World Congress on Intelligent Control and Automation, 2004. WCICA 2004, vol. 5, pp. 4282-4286, June 2004.
  12. S. E. Schaeffer, "Graph clustering," Computer Science Review, vol. 1, pp. 27-64, Aug. 2007.
  13. S. van Dongen, Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, May 2000.
  14. MAWI Working Group, "MAWI Working Group Traffic Archive. " http://mawi. wide. ad. jp.
  15. CAIDA, "Coralreef software suite. " http://www. caida. org/tools/measurement/coralreef/.
  16. "Graphviz: Open source graph visualization software. " http://www. graphviz. org/.
Index Terms

Computer Science
Information Sciences

Keywords

Graph clustering Traffic dispersion graph Markov clustering HTTP