Trojan Detection Simulation Group under the Cloud Computing Environment

Article Preview

Abstract:

The accurate method for Trojan group detection under cloud computing environment is studied in this paper. For the problem of Trojan group detection under the cloud computing environment, this paper proposed a Trojan group detection method based on the BP neural network. BP neural network model is constructed and the problem of Trojan group detection is acted on in this model. Experimental results show that the use of this algorithm for Trojan group detection can get the accurate detection results. Thus, it can open the network protection mode to ensure the user's network security and prevent the information loss caused by that the network is intruded.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1996-1999

Citation:

Online since:

August 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

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

Google Scholar

[2] 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

Google Scholar

[3] 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

Google Scholar

[4] D. Subhadrabandhu, F. Anjum, and S. Sarkar. On optimal placement of intrusion detection modules in sensor networks[C]. Proceedings of the First International Conference on Broadband Networks, 2004: 690-699.

DOI: 10.1109/broadnets.2004.52

Google Scholar

[5] B Waters. Efficient identity-based encryption without random ora-cles[C]. Advances in Cryptology-EUROCRYPT 2005. Berlin: Springer-Verlag, 2005. 114-127.

DOI: 10.1007/11426639_7

Google Scholar

[6] Everthon Silva Fonseca, Rodrigo Capobianco Guido, Paulo Rogério Scalassara, Carlos Dias Maciel, José Carlos Pereira. Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders [J]. Computers in Biology and Medicine, 2007, 37(4): 571-578.

DOI: 10.1016/j.compbiomed.2006.08.008

Google Scholar