DocumentCode
3599832
Title
Research on the analytic factor neuron model based on cloud generator and its application in oil&gas SCADA security defense
Author
Yong Qin ; Xiedong Cao ; Peng Liang ; Qichao Hu ; Weiwei Zhang
Author_Institution
Mechatron. Eng. Coll., Southwest Pet. Univ., Chengdu, China
fYear
2014
Firstpage
155
Lastpage
159
Abstract
This paper puts forward an analytic factor neuron model which combines reasoning machine based on the cloud generator with the Factors Neural Network theory. The factor neuron model is realized based on mobile intelligent agent and malicious behavior perception technology. Therefore, it, compared with the traditional security defense technology, has strong flexibility, mobility, scalability and other features. At the same time, the model has a cross-platform characteristics and distributed characteristics, which is suitable for the demand of the SCADA system security defense. This paper builds a neural network with a group of neurons model. By configuring each factor neuron model in the neural network, the safety of SCADA system can be protected effectively.
Keywords
SCADA systems; control engineering computing; inference mechanisms; mobile agents; natural gas technology; neurocontrollers; oil technology; production engineering computing; security; SCADA system; analytic factor neuron model; cloud generator; cross-platform characteristics; factors neural network theory; malicious behavior perception technology; mobile intelligent agent; neural network; oil&gas SCADA security defense; reasoning machine; security defense; security defense technology; Analytical models; Automata; Computers; Databases; Generators; Neurons; Security; Analytic factor neuron; Mobile intelligent agent; Oil&gas SCADA security defense; cloud generator; malicious behavior perception;
fLanguage
English
Publisher
ieee
Conference_Titel
Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on
Print_ISBN
978-1-4799-4720-1
Type
conf
DOI
10.1109/CCIS.2014.7175721
Filename
7175721
Link To Document