International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 6, No. 8, 8 Jul. 2014
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Security Visualization Analytics Model in Online Social Networks Using Data Mining and Graphbased Structure Algorithms
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Author(s)
Prajit Limsaiprom
1,*
Prasong Praneetpolgrang
1
Pilastpongs Subsermsri
1
Index Terms
Anomaly and Cyber-Attacks, Influencing Nodes, Online Social Networks, Graph-based Structure Algorithm, Classification Algorithm
Abstract
The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.
Cite This Paper
Prajit Limsaiprom, Prasong Praneetpolgrang, Pilastpongs Subsermsri, "Security Visualization Analytics Model in Online Social Networks Using Data Mining and Graph-based Structure Algorithms", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.8, pp.1-10, 2014. DOI:10.5815/ijitcs.2014.08.01
Reference
[1]P. Limsaiprom and P. Tantatsanawong, Study of Computer Virus Distribution in Social Network: A case Study of National Blood Centre, Thai Red Cross Society, Proceedings of the National Conference on Computer Information Technologies, 2010: pp.115-120.
[2]P. Limsaiprom and P. Tantatsanawong, Social Network Anomaly and Attack Patterns Analysis, Proceedings of the sixth International Conference on Networked Computing, 2010: pp.136-141.
[3]P.Limsaiprom and P.Praneetpolgrang, Tracking the Influencing Nodes of Anomaly and attack Patterns in Social Networks, Proceedings of ICSEC, 2011: pp.115-120.
[4]C. Haythornthwaite, Social Network Analysis: An Approach and Technique for the Study of Information Exchange, Proceeding of the ALISE conference, 1996: pp.323-342.
[5]T. Tylenda, R. Angelova, S. Bedathur, Towards Time-aware Link Prediction in Evolving Social Networks, Proceeding of The third SNA-KDD Workshop, 2009.
[6]Z. Huang, Link Prediction Based on Graph Topology: The Predictive Value of the Generalized Clustering Coefficient, Proceeding of LinkKDD, 2006.
[7]R.N. Lichtenwalter, J.T. Lussier, N.V. Chawla, New Perspectives and Methods in Link Prediction, Proceeding of KD: 2010.
[8]W.K. Sharabati, E.J. Wegman, Y.H. Said, Predicting Edges And Vertices In A Network, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010: pp.71-74.
[9]S. Kaza, D. Hu, H. Chen, Dynamic Social Network Analysis of a Dark Network: Identifying Significant Facilitators, IEEE Journal, 2007: pp.40-46.
[10]B. Bringmann, M. Berlingerio, Learning and Predicting the Evolution of Social Networks, IEEE Intelligent Systems Journal, 2010: pp.1541-1672.
[11]C. Barrett, H.B. Hunt III, M. V. Marathe, S.S. Ravi, D.J. Rosenkrantz, R.E. Stearns, Modeling and analyzing social network dynamics using stochastic discrete graphical dynamical systems, Theoretical Computer Science Journal, vol. 412, 2011: pp.3932-3946.
[12]L. Zhou, L. Ding, T. Finin, How is Semantic Web evolving? A dynamic social network perspective, Computers in Human Behavior Journal, vol.27, 2011: pp.1294-1302.
[13]S. Aral, D. Walker, Identifying Influential and Susceptible Individuals in Social Networks: Evidence from a Randomized Experiment, Proceedings of WISC, 2010: pp.1-7.
[14]E. Bakshy, I. Rosenn, C. Marlow, C. Adamic, The Roles of Social Networks in Information Diffusion, Proceedings of IW3C2, 2012: pp.519-528.
[15]M. Kimura, K. Yamakawa, K. Saito, and H. Motoda, Community Analysis of Influential Nodes for Information Diffusion on a Social Network, IEEE Xplore, 2009: pp.1358-1363.
[16]T. Fushimi, T. Kawazoe, K. Saito, M. Kimura, and H. Motoda, What does an Information Diffusion Model Tell about Social Network Structure, PKAW, D. Richards, and B-H. Kang, Eds. Berlin, Germany: Springer-Verlag, 2009: pp.122-136.
[17]A. Plabo, V. Pablo, and K. Saito, Selecting the Most Influential Nodes on Social Networks, International Joint Conference on Neural Networks, Proceeding, 2007: pp. 2397-2402.
[18]C. Azad and V. K. Jha, “Data Mining in Intrusion Detection: A Comparative Study of Methods, Type and Data Sets,” Journal of IJITCS, vol. 5, no. 8, pp. 75-90, July 2013.
[19]S. Sharma and G. N. Purohit, “A New Centrality Measure for Tracking Online Community in Social Netowrks,” Journal of IJITCS, vol. 4, no. 4, pp. 47-53, April 2012.
[20]K. Siato, M. Kimura, K. Ohara, and H. Motoda, Learning Continous-Time Information Diffusion Model for Social Behavioral Data Analysis, In ACML, Z.-H. Zhou, and T. Washio, Eds. Berlin, Germany: Springer-Verlag, 2009: pp.322-337.