The Intrusion Detection Model of Medical Information Diversity Database under Cloud Computing

Abstract:

In current large databases, there is a widespread defect of poor immunity. It is mainly due to the huge amount of data which makes the traditional algorithm falling into a defect of low efficiency of local search, causing inconspicuous effect to detect potential risks and other defects. To this end, we propose an intrusion detection model of medical information diversity database under cloud computing. By using the similar risk attributes of data, an intrusion test model under cloud computing environment is constructed. The use of intrusion tolerance theory can reduce the impact of failure report phenomenon for the stability of whole system. This model can effectively resist denial of service attacks, but also through the mutual cooperation between the servers to prevent the single failure node attacking to the system, ensure the safety and smooth of the cloud computing services at the greatest degree, in order to enhance the user experience. Experiments show that the algorithm improves the accuracy of the intrusion detection of medical information diversity database, and achieves good results.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 2914-2918
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.2914
• Online since: May 2014
• Authors: Qi Lai Zhang, Pei Cai Cong*, Fan Bo Meng, Jian Ping Yang, Bin Wang
• Keywords: Characteristic Parameters, Cloud Computing, Intrusion Detection

References

[1] Tarun Ambwani. Multi Class Support Vector Machine Implementation to Intrusion detection[C]. In: 2003 International Joint Conference on Neural Network, 2003: 2300-2305.

DOI: 10.1109/ijcnn.2003.1223770

[2] Perrig A, Stankovic J, Wagner D. Security in wireless sensor networks[J]. CACM, June 2004, 47: 53–57.

[3] Anjum F, Subhadrabandhu D, Sarkar S, et al. On Optimal Placement of Intrusion Detection Modules in Sensor Networks[C]. 1st International Conference on Broadband Networks. Washington: IEEE Press , 2004: 433–439.

DOI: 10.1109/broadnets.2004.52

[4] Qing Li, Zhu Qingxin, Wang Mingwen. A distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network [J]. Journal of Software, 2006, 17(3): 481-489.

DOI: 10.1360/jos170481

[5] Bandyopadhyay S, Giannella C, Maulik U, et al. Clustering distributed data streams in peer-to-peer environments[J]. Information Sciences, 2006, 176 : 1952–(1985).

DOI: 10.1016/j.ins.2005.11.007

[6] Onat I and Miri A. An intrusion detection system for wireless sensor networks[J]. Wireless And Mobile Computing Networking And Communications, August 2005, 3: 253–259.

DOI: 10.1109/wimob.2005.1512911