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Optimization of Security Communication Wired Network by Means of Genetic Algorithms

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DOI: 10.4236/cn.2012.43024    3,018 Downloads   5,550 Views   Citations

ABSTRACT

The realization of security wired network is very critical when the network itself must be installed in an environment full of restrictions and constrains such as historical palaces, characterized by unique architectural features. The purpose of this paper is to illustrate an advanced installation design technique of security wired network based on genetic algorithm optimisation that is capable of ensuring high performances of the network itself and significant reduction of the costs. The same technique can be extended to safety system such as fire signalling.

Cite this paper

F. Garzia, N. Tirocchi, M. Scarpiniti and R. Cusani, "Optimization of Security Communication Wired Network by Means of Genetic Algorithms," Communications and Network, Vol. 4 No. 3, 2012, pp. 196-204. doi: 10.4236/cn.2012.43024.

References

[1] F. Garzia and R. Cusani, “The Integrated Safety/Security/ Communication System of the Gran Sasso Mountain in Italy,” International Journal of Safety & Security Engineering, WIT Press, Southampton and Boston.
[2] F. Garzia, E. Sammarco and R. Cusani, “The Integrated Security System of the Vatican City State,” International Journal of Safety & Security Engineering, Vol. 1, No. 1, 2011, pp. 1-17. doi:10.2495/SAFE-V1-N1-1-17
[3] G. Contardi, F. Garzia and R. Cusani, “The Integrated Security System of the Senate of the Italian Republic,” International Journal of Safety & Security Engineering, Vol. 1, No. 3, 2011, pp. 219-246.
[4] L. Davis, “Genetic Algorithms and Simulated Annealing,” Morgan Kaufmann Publishers, Inc., Los Altos, 1987.
[5] L. Davis, “Handbook of Genetic Algorithm,” Van Nostrand Reinhold, New York, 1991.
[6] A. H. F. Dias and J. A. de Vasconcelos, “Multiobjective Genetic Algorithms Applied to Solve Optimization Problems,” IEEE Transactions on Magnetics, Vol. 38, No. 2, 2002, pp. 1133-1136. doi:10.1109/20.996290
[7] D. E. Goldberg, “Genetic Algorithms in Search, Optimisation and Machine Learning,” Addison-Wesley, New York, 1989.
[8] D. E. Goldberg and K. Deb, “Foundations of Genetic Algorithms,” Morgan Kaufmann, New York, 1991.
[9] J. H. Holland, “Genetic algorithms,” Scientific American, Vol. 267, 1992, pp. 66-72. doi:10.1038/scientificamerican0792-66
[10] M. Crosbie and G. Spafford, “Applying Genetic Programming to Intrusion Detection,” Proceedings of AAAI Fall Symposium on Genetic Programming, Cambridge, 1995, pp. 1-8.
[11] A. Munoz, S. Martorell and V. Serradell, “Genetic Algorithms in Optimizing Surveillance and Maintenance of Components,” Reliability Engineering and Systems Safety, Vol. 57, No. 2, 1997, pp. 107-120. doi:10.1016/S0951-8320(97)00031-8
[12] S. M. Bridges and R. B. Vaughn, “Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection,” Proceedings of NISSC—National Information Systems Security Conference, Baltimore, 2000, pp. 230-244.
[13] S. M. Bridges and R. B. Vaughn, “Intrusion Detection via Fuzzy Data Mining,” Proceedings of Canadian Information Technology Security Symposium, Ottawa, 2000, pp. 347-358.
[14] J. Luo and S. Bridges, “Mining Fuzzy Association Rules and Fuzzy Frequency Episodes,” International Journal of Intelligent Systems, Vol. 15, No. 1, 2000, pp. 156-161.
[15] A. Siraj, R. B. Vaughn and S. M. Bridges, “Decision Making for Network Health Assessment in an Intelligent Intrusion Detection System Architecture,” Journal of Information and Decision Making, Vol. 3, 2004, pp. 458-463.
[16] N. Basha, I. Bharanindharan and A. M. Ahmed, “Hybrid Intelligent Intrusion Detection System,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 6, 2005, pp. 291-294.
[17] C. M. Lin and M. Gen, “An effective Decision-Based Genetic Algorithm Approach to Multiobjective Portfolio,” Applied Mathematical Sciences, Vol. 1, 2007, pp. 201-210.
[18] H. Huang, R. Ooka, H. Chen and S. Kato, “Optimum Design for Smoke-Control System in Buildings Considering Robustness Using CFD and Genetic Algorithms,” Building and Environment, Vol. 44, 2009, pp. 2218-2227. doi:10.1016/j.buildenv.2009.02.002
[19] M. Saniee, J. Habibi and C. Lucas, “Intrusion Detection Using a Fuzzy Genetics-Based Learning Algorithm,” Journal of Network and Computer Applications, Vol. 30, No. 1, 2007, pp. 414-428. doi:10.1016/j.jnca.2005.05.002

  
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