Complex information system security risk propagation research based on cellular automata

Li, Zhao; Xu, Guoai; Xiaofang, Ban; Zhang, Yi; Zheng-ming, HU

doi:10.7498/aps.62.200203

Cited by 9 publications

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Research on optimal control model of complex networks security risk

Ren

Zhang

et al. 2019

J. Phys.: Conf. Ser.

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To solve the problems such as the security risk control and the optimization of the complex networks, an optimal control model of complex networks security risk based on discrete glowworm swarm optimization with key strategy adjustment is proposed. The basic framework of this model is built based on four functional modules including detection and evaluation, strategy selection, control optimization and performance feedback. The optimal evaluation criterions of the security control strategy are constituted of factors such as control cost, benefit reward and negative effect. The discrete glowworm swarm optimization algorithm with key strategy ad-justment is proposed to search for the optimal control strategy of complex network security risk. The concept of control parameter is introduced, whose feedback and adjustment make the optimal control model get evolution-ary. Finally, the model and the algorithm are tested for network security risk optimal control in the Nearest-Neighbor coupled with network, Erdos-Renyi random graph network, Watts-Strogatz small world network and Ba-rabasi-Albert power law network, separately. The availability of the model and the superiority of the algorithm are validated and the effect of the change for the attack strategy to the security risk optimal control model is analyzed by simulations.

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Research on optimal control model of complex networks security risk

Ren

Zhang

et al. 2019

J. Phys.: Conf. Ser.

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An Information Source Localization Algorithm Based on Node Reachability Measurement

Zhang

Guo

Shen

et al. 2018

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

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An information source localization algorithm based on cellular automata model

Zhang

Guo

Shen

et al. 2019

Int. J. Mod. Phys. B

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In order to research the localization problem of information source in a social network, this paper first constructs a complex network node model (Random-Susceptible-Infected-Recovered, R-SIR) based on cellular automata theory, and then utilizes this model to calculate the number of forwarding paths from a node to other forwarding nodes and the probability of occurrence of such forwarding paths, and presents an information source search method based on node reachability measure to locate the information distribution source in the information forwarding network. Theoretical analysis indicates that the algorithm, on the one hand, can avoid the time complexity problem brought by the maximum likelihood estimation, and on the other hand, overcome the inaccuracy problem of step number estimation in accessibility measure. Experimental results show that the algorithm has higher recognition rate and recognition accuracy for the information source in a multi-node network, and can search the information source, and is of great significance for the privacy protection of personal data.

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Complex information system security risk propagation research based on cellular automata

Cited by 9 publications

References 0 publications

Research on optimal control model of complex networks security risk

Research on optimal control model of complex networks security risk

An Information Source Localization Algorithm Based on Node Reachability Measurement

An information source localization algorithm based on cellular automata model

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