Self-Organized Detection of Relationships in a Network

Qurban A. Memon

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DOI: 10.4236/ijcns.2010.33039                

Abstract

Multistate operations within a network result in high-dimensional, multivariate temporal data, and are useful for systems, which monitor access to network entities like resources, objects, etc. Efficient self organization of such multi-state network operations stored in databases with respect to relationships amongst users or between a user and a data object is an important and a challenging problem. In this work, a layer is proposed where discovered relationship patterns amongst users are classified as clusters. This information along with attributes of involved users is used to monitor and extract existing and growing relationships. The correlation is used to help generate alerts in advance due to internal user-object interactions or collaboration of internal as well as external entities. Using an experimental setup, the evolving relationships are monitored, and clustered in the database.

Keywords

Relationship Network, Network Access, Self-Organization in Networks, Relationship Clustering

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	T. Shinohara, “Ubiquitous network vision for new growth,” ITU Telecom Report, 2003.
[2]	L. Srivastava, “The network of the future: What is over the horizon,” Billing Asia 2006, Shanghai, China, 13 March 2006.
[3]	Y. Zhang and W. Lee, “An integrated environment for testing mobile ad-hoc networks,” Proceedings of 3rd ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), June 2002.
[4]	R. Fernandez, “Enterprise dynamic access control (EDA C) case study,” CS1/06-0078 Project, United States Navy, March 2006.
[5]	National Institute of Standards and Technology-RBAC. http://csrc.nist.gov/rbac/.
[6]	D. Hammerstrom, “Neural networks at work,” IEEE Spectrum, pp. 26–32, June 1993.
[7]	Aleksander, et al., “An Introduction to neural computing,” Chapman and Hall, 1990.
[8]	N. Ye, X. Li, Q. Chen, S. Emran, and M. Xu, “Probabilistic techniques for intrusion detection based on computer audit data,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 31, No. 4, pp. 266–274, 2001.
[9]	R. Fuller and M. Kantardzic, “Distributed monitoring of frequent items,” Transactions on Machine Learning and Data Mining, Vol. 1, No. 2, pp. 67–82, 2008.
[10]	F. Anjum, D. Subhadrabandhu, and S. Sarkar, “Signature based intrusion detection for wireless ad hoc networks: A comparative study of various routing protocols,” IEEE Vehicular Technology Conference, Orlando, FL, Vol. 58, pp. 2152–2156, 6–9 October 2003.
[11]	A. Mishra, K. Nadkarni, and A. Patcha, “Intrusion detection in wireless ad hoc networks,” IEEE Wireless Communications, pp. 48–60, February 2004.
[12]	D. Subhadrabandhu, S. Sarkar, and F. Anjum, “Efficacy of misuse detection in ad hoc networks,” Proceedings of the Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, pp. 97–107, 2004.
[13]	T. Chen and V. Venkataramanan, “Dempster-Shafer theory for intrusion detection in ad hoc networks,” IEEE Internet Computing, pp. 35–41, December 2005.
[14]	T. Otto, A. Meyer-Baese, M. Hurdal, D. Sumners, D. Auer, and A. Wismuller, “Model-free functional MRI analysis using cluster-based methods,” Proceedings of SPIE, Vol. 5103, pp. 17–24, August 2003.
[15]	S. Przylucki, W. Wojcik, K. Plachecki, and T. Golec, “An analysis of self-organization process for data classification in multisensor systems,” Proceedings of SPIE, Vol. 5124, pp. 325–332, September 2003.
[16]	P. D’Urso and L. Giovanni, “Temporal self-organizing maps for telecommunications market segmentation,” Neurocomputing, Vol. 71, pp. 2880–2892, 2008.
[17]	T. Voegtlin and P. Dominey, “Recursive self-organizing maps,” Neural Networks, Vol. 15, No. 8–9, pp. 979–991, 2002.
[18]	B. Hammer, A. Micheli, A. Sperduti, and M. Strickert, “Recursive self-organizing network models,” Neural Networks, Vol. 17, No. 8–9, pp. 1061–1085, 2004.
[19]	Y. Shidara, M. Mineichi Kudo, and A. Nakamura, “Classification based on consistent itemset rules,” Transactions on Machine Learning and Data Mining, Vol. 1, No. 1, pp. 17–30, 2008.
[20]	A. Fornells, E. Armengol, E. Golobardes, S. Puig, and J. Malvehy, “Experiences using clustering and generalizations for knowledge discovery in melanomas domain,” Transactions on Machine Learning and Data Mining, Vol. 1, No. 2, pp. 49–65, 2008.
[21]	K. Rosen, “Discrete mathematics and its applications,” 4th Edition, McGraw-Hill Publishers, 2000.
[22]	V. Krebs, “Introduction to social network analysis,” 15 January 2007. http://www.orgnet.com/sna.html.