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首页> 外文期刊>Big Data and Cognitive Computing >Ensuring SDN Resilience under the Influence of Cyber Attacks: Combining Methods of Topological Transformation of Stochastic Networks, Markov Processes, and Neural Networks
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Ensuring SDN Resilience under the Influence of Cyber Attacks: Combining Methods of Topological Transformation of Stochastic Networks, Markov Processes, and Neural Networks

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摘要

The article proposes an approach to ensuring the functioning of Software-Defined Networks (SDN) in cyber attack conditions based on the analytical modeling of cyber attacks using the method of topological transformation of stochastic networks. Unlike other well-known approaches, the proposed approach combines the SDN resilience assessment based on analytical modeling and the SDN state monitoring based on a neural network. The mathematical foundations of this assessment are considered, which make it possible to calculate the resilience indicators of SDN using analytical expressions. As the main indicator, it is proposed to use the correct operation coefficient for the resilience of SDN. The approach under consideration involves the development of verbal models of cyber attacks, followed by the construction of their analytical models. In order to build analytical models of cyber attacks, the method of topological transformation of stochastic networks (TTSN) is used. To obtain initial data in the simulation, the SDN simulation bench was justified and deployed in the EVE-NG (Emulated Virtual Environment Next Generation) virtual environment. The result of the simulation is the time distribution function and the average time for the cyber attack implementation. These results are then used to evaluate the SDN resilience indicators, which are found by using the Markov processes theory. In order to ensure the resilience of the SDN functioning, the article substantiates an algorithm for monitoring the state of controllers and their automatic restructuring, built on the basis of a neural network. When one is choosing a neural network, a comparative evaluation of the convolutional neural network and the LSTM neural network is carried out. The experimental results of analytical modeling and simulation are presented and their comparative evaluation is carried out, which showed that the proposed approach has a sufficiently high accuracy, completeness of the obtained solutions and it took a short time to obtain the result.

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