A Novel Cluster Selection Algorithm in the NIDS of Wireless Ad Hoc

Xue Hui Li; Zi Yu Liu

doi:10.4028/www.scientific.net/AMM.599-601.1914

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601A Novel Cluster Selection Algorithm in the NIDS of...

A Novel Cluster Selection Algorithm in the NIDS of Wireless Ad Hoc

Abstract:

The article presents a novel cluster selection algorithm from the point of the network security. The measure uses Kruskal algorithm to calculate the minimum spanning tree of the topological structure of wireless Ad Hoc networks and builds clustering on it which ensures the cost of communication among cluster nodes that keeps in a relatively low level. The algorithm for the NIDS (network intrusion detection system) of wireless Ad hoc provides a stable and effective support and enhances the survivability of the system. The result of simulation experiment shows that the measure has good performance.

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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1914-1917

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1914

Citation:

Cite this paper

Online since:

August 2014

Authors:

Xue Hui Li*, Zi Yu Liu

Keywords:

Ad Hoc, Cluster Selection Algorithm, Intrusion Detection

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* - Corresponding Author

References

[1] Wu M H, Xiong X H, Ying J, et al. QoS-driven global optimization approach for large-scale web services composition. Journal of Computers, Vol. 6 (2011), pp.1452-1460.

DOI: 10.4304/jcp.6.7.1452-1460

Google Scholar

[2] Yu T, Zhang Y, Lin K J. Efficient algorithms for web services selection with end-to-end QoS constraints. Association for Computing Machinery Trans. on the Web, Vol. 1 (2007), pp.1-26.

DOI: 10.1145/1232722.1232728

Google Scholar

[3] Zhao X C, Song B Q, Huang P Y. An improved discrete immune optimization algorithm based on PSO for Qos-driven web service composition. Applied Soft Computing, Vol. 12 (2012), pp.2208-2216.

DOI: 10.1016/j.asoc.2012.03.040

Google Scholar

[4] Tsaia S J, Suna T Y, Liub C C, et al. An improved multi-objective particle swarm optimizer for multi-objective problems. Expert Systems with Applications, Vol. 37 (2010), pp.5872-5886.

DOI: 10.1016/j.eswa.2010.02.018

Google Scholar

[5] Wang W B, Sun Q B, Zhao X C, et al. Web services selection approach with QoS global optimal based on discrete particle swarm optimization with non-uniform mutation algorithm. Acta Electronica Sinica, Vol. 38 (2010) pp.2774-2779.

Google Scholar

[6] Konak A, Coit D W, Smith A E. Multi-objective optimization using genetic algorithms: a tutorial. Reliability Engineering & System Safety, Vol. 91 (2006), pp.992-1007.

DOI: 10.1016/j.ress.2005.11.018

Google Scholar

[7] Marlon D, Luciano G B, Artem P, et al. Aggregate quality of service computation for composite services. Proc. of the International Conference on Service Oriented Computing, (2010), pp.213-227.

Google Scholar

[8] Praveen K T, Sanghamitra B, Sankar K P. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Information Sciences, Vol. 177 (2007), pp.5033-5049.

DOI: 10.1016/j.ins.2007.06.018

Google Scholar