Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods-Reference-Cited by-同舟云学术

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Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Published:2017-02-23 Issue:3 Volume:30 Page:1111-1123
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Wu Mingtao,Song Zhengyi,Moon Young B.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/article/10.1007/s10845-017-1315-5/fulltext.html

Reference33 articles.

1. Bosch, A., Zisserman, A., & Munoz, X. (2007). Image classification using random forests and ferns. In 2007 IEEE 11th international conference on computer vision, pp. 1–8.

2. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. ACM Computing Surveys, 41(3), 1–58.

3. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computig Surveys, 41(3), 1–58.

4. Duro, J. A., Padget, J. A., Bowen, C. R., & Kim, H. A. (2016). Multi-sensor data fusion framework for Cnc machining monitoring. Mechanical Systems and Signal Processing, 67, 505–520.

5. Garcia, R. F., Rolle, J. L. C., & Castelo, J. P. (2011). A review of SCADA anomaly detection systems. Advances in Intelligent and Soft Computing, 87, 405–414.

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