Data Storm Monitoring System of Large Power Industrial Control Network under Non Uniform Format

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Abstract:

In the high power industrial control network, due to the large user base, the data traffic is plodding, the data formats are inconsistent, the network data storm is difficult to monitor. A kind of high power industrial control network data monitoring and control system is designed under non-uniform formats. The network node information collector is designed, the data is collected, and the data collection result is taken as the mathematical expectation, the mathematical expectation is trained. The problem of data flow storm statistics is solved. In the system, the format conversion function is added, data storm monitoring results is taken with the format conversion, and they are stored as text format. The monitoring program reads and displays the result, the difficulties brought by the format is not unified are solved. System test results show that the system can monitor the large power industrial control network data storm, the monitoring result is precise, and it can make the formatting process fast. A size of the generated result file is 18.1%, of the original file, and can fully reflect the network data storm characteristics, the effect is perfect.

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Periodical:

Advanced Materials Research (Volumes 989-994)

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2970-2974

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Online since:

July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] ZHANG Ren-shang. Network Intrusion Detection System Based on Expert System and Neural Network[J]. Computer Simulation. 2012; 29(9): 162-165.

Google Scholar

[2] Ke Hua-mingl, Chen Chao-zhenl, etc. Application of BP neural network classification with optimization of genetic algorithm for remote sensing imagery based on Matlab[J]. Journal of Southwest University of Science and Technology, 2010, 3(2): 127-131.

Google Scholar

[3] Zhu Hong. Fault Diagnosis for Analog Circuits Based on D-S Evidence Theory and PSO Neural Network[J]. Computer Measurement & Control. 2013; 21(4): 868-870.

Google Scholar

[4] Zhao Juan, Zhao Qiang, Wu Fenxia. Self-organizing Map Neural Network Based Image Retrieval Algorithm[J]. Bulletin of Science and Technology. 2013; 29(2): 55-57.

Google Scholar

[5] SHAN Dong-hong, ZHAO Wei-ting. Research on Intrusion Detection System Neural Networks and Principal Component Analysis[J]. Computer Simulation. 2011; 28(6): 153-156.

Google Scholar