Title :
Research on Network Attack and Defense of SCADA System Model Based on FNN
Author :
Tao Yu ; Xiedong Cao ; Zhidi Chen ; Chela Zhang
Author_Institution :
Sch. of Electr. Eng. & Inf., Southwest Pet. Univ., Chengdu, China
Abstract :
In order to guarantee the safety operation of the SCADA system under network attack condition, it is important to construct an intelligent model on SCADA system including reasoning and judgment in network attack and defense. This paper describes the network attack knowledge based on the theory of the factor expression of knowledge, and studies the formal knowledge theory of SCADA network from the factor state space, equivalence partitioning, etc. It utilizes the factor neural network (FNN) theory which contains high-level knowledge and quantitative reasoning described to establish a predictive model including analytic FNN and analogous FNN. This model abstracts and builds an equivalent and corresponding network attack and defense knowledge factors system. Analysis shows that the network attack and defense strategy model of SCADA system according to the FNN has effective security defense performance in network attack, and it provides new methods of researching the security defense theory of SCADA system under the condition of network attack.
Keywords :
SCADA systems; neural nets; security of data; SCADA system model; analogous FNN; analytic FNN; defense knowledge factor system; factor expression tbeory; factor neural network theory; formal knowledge theory; high-level knowledge; intelligent model; network attack knowledge; predictive model; quantitative reasoning; security defense performance; security defense theory; Analytical models; Biological neural networks; Cognition; Knowledge engineering; Neurons; SCADA systems; Security; SCADA; factor expression; factor neural network; network attack and defense; security defense;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on
Conference_Location :
Shiyang
DOI :
10.1109/ICCIS.2013.374