A Network Troubleshooting Method Based on Dempster Rule

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In this paper, a new method which is named DRNFD for network troubleshooting is brought forward in which “abnormal degree” is defined by the vector of probability and belief functions in a privileged process. A new formula based on Dempster Rule is presented to decrease false positives. This method (DRNFD) can effectively reduce false positive rate and non-response rate and can be applied to real-time fault diagnosis. The operational prototypical system demonstrates its feasibility and gets the effectiveness of real-time fault diagnosis.

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2611-2617

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November 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Oppenheimer, D., Archana Ganapathi, and David A. Patterson. Why do Internet services fail, and what can be done about it? The 4th USENIX Symposium on Internet Technologies and Systems (USITS '03), March (2013).

Google Scholar

[2] Jagannathan R, Neumann P, Javitz H, Valdes A, Garvey T. A Real-Time Fault Diagnosis Expert System(FDES). Final Technical Report, Computer Science Laboratory, SRI International, Menlo Park, Calif., Feb. (2012).

Google Scholar

[3] Haas R, Droz P, Stiller B. Autonomic service deployment in networks. IBM Systems Journal, 2003, 42(1): 150-164.

DOI: 10.1147/sj.421.0150

Google Scholar

[4] D. Gavalas, D. Greenwood, M. Ghanbari, etc. Advanced Network Monitoring Applications Based on Mobile/Intelligent Agent Technology. Computer Communications, 2000, 23(8): 720-730.

DOI: 10.1016/s0140-3664(99)00232-7

Google Scholar

[5] R. Tagliaferri, A. Eleuteri, M. Meneganti, F. Barone. Fuzzy Min-Max Neural Network: from Classification to Regression. Soft Computing, 2001, 5(1): 69-76.

DOI: 10.1007/s005000000067

Google Scholar

[6] Basseville M, Nikiforov I V. Detection of Abrupt Changes-Theory and Application. Englewood Cliffs: Prentice-Hall, (1993).

Google Scholar

[7] Rolf Iserman. Process Fault Detection Based on Modeling and Estimation and Knowledge Processing-Tutorial Paper. Automatic, 1999, 29(4): 815-835.

Google Scholar

[8] Liu Feng Yu, etc. A Distributed Network Troubleshooting Model Based on Bayesian Classification. Journal of Nanjing University of Science and Technology, 2003, 27(5): 546-551.

Google Scholar

[9] Zhang Wen Xiu, Mi Ju Sheng, etc. The Knowledge Reduction of Uncoordinated Target Information System, Journal of Computer, 2003, 26(1): 12-18.

Google Scholar

[10] Zhou Zhi Hua. Neural Network Ensemble. Journal of Computer, 2002, 25(1): 1-8.

Google Scholar