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This paper firstly analyses the security vulnerability of oil and gas SCADA systems. Combing the SCADA Security Defense Model (FSDM) based on factor neural network, the method to realize the function of the recognition module (the recognition module is a significant part of the IN neurons as the core of the FSDM) based on dynamic fuzzy neural network (DFNN) is proposed. The integration of dynamic character is to solve difficulty to make the SCADA system defensing rules which is hard for the domain experts. Fuzzy neural network can not only solve the fuzzy problems in malicious program judgment but also meet the real-time and rapid requirements in oil and gas SCADA system. As oil and gas SCADA security defense research is still in its infancy, dynamic fuzzy neural network which is proposed in this paper to solve oil and gas SCADA security defense problems provides a new solution for the future and lays the theoretical foundation.

Exploration and Research on Oil and Gas SCADA Security Defense Based on Dynamic Fuzzy Neural Network (DFNN)