Clustering wireless sensor network malicious program propagation model based on evolution game

Abstract

The invention relates to a clustering wireless sensor network malicious program propagation model based on evolution game, and the model comprises three system models: a wireless sensor network model, an improved two-dimensional cellular automaton malicious program propagation model, and a multi-person evolution game model. On the basis of considering the node distribution density of a wireless sensor network topological structure, the invention proposes the clustering wireless sensor network cellular automaton malicious program propagation model based on multi-person evolution game.

Description

technical field [0001] The invention belongs to the field of wireless sensor networks, and relates to mathematical modeling, in particular to a cluster wireless sensor network malicious program propagation model based on evolutionary game. Background technique [0002] Wireless sensor network is a network formed by a large number of sensor nodes with limited power supply, computing and wireless communication capabilities through self-organization, and broadcasts sensor monitoring data in the form of multi-hop throughout the network. The application range of wireless sensor networks has gradually spread from light, temperature, and sound measurement to advanced military, industry and life. As the sensitivity and value of collected information become higher and higher, the security problems of wireless sensor networks are also becoming more and more more and more important. At present, research on the propagation of malicious programs in wireless sensor networks is considered...

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Problems solved by technology

[0004] Said. Hayam et al. Using signal processing technology to establish a worm propagation model in wireless sensor networks [J]. IEEE Signal Processing Journal, 2006, 23(2): 164-169. A method using signal processing is proposed. Technology establishes a topology-aware worm propagation model to analyze worm propagation characteristics from the perspective of time and space, but the epidemic model of worms is not considered enough

[0005] Pradeep. De et al. Modeling Node Compromise in Wireless Sensor Networks Using Contagion Theory [C]. 2006 International Symposium on Wireless, Mobile and Multimedia Networks World IEEE Proceedings, pp.237-243. The potential node compromise problem, using the epidemic theory to study the node damage propagation process in the wireless sensor network, and then established a random graph-based model, pointed out the key reasons for determining the virus outbreak, but still in describing the dynamics of malware propagation behavior There are deficiencies

[0006] Song Yurong, Jiang Guoping. Using Cellular Automata to Model Malware Propagation in Wireless Sensor Networks [C]. IEEE International Conference on Neural Networks and Signal Processing, Zhejiang, China, 2008.6:623-627 Using Cellular Automata to Establish The propagation model of malicious programs in wireless sensor networks, which reflects the spatio-temporal characteristics of the propagation process of malicious programs, but the model lacks the research on the differences of nodes in wireless sensor networks

[0007] Yang Xiong et al. Research on the propagation model of malware in wireless sensor networks based on node difference[J]. Computer Applied Research, 2012,29(1):316-321. On the basis of two-dimensional cellular automata, a node difference is proposed The malicious program propagation model, which introduces the MAC wireless channel contention mechanism and the neighborhood communication distance factor, describes the influence of node difference on the spread of malicious software in wireless sensor networks. However, the prevalence of malicious programs in the research process The state of the disease model is not considered enough

[0008] Fu Shuai et al. Malware Propagation Model in Wireless Sensor Networks [J]. Computer Engineering, 2011, 37(3): 129-131. Based on the epidemiological theory, a sleep and wake-up mechanism was added, and a wireless sensor The SIR / WS model of malicious program propagation in the network, which improves the immunity rate of the network and reduces the infection rate of the network, but this model only describes the overall network and lacks an effective reflection of the local and micro spatio-temporal characteristics

[0009] Feng Liping et al. Worm Propagation Modeling and Stability Analysis in Wireless Sensor Networks[J]. Engineering Mathematics Issues, 2015, paper number: 129598, 8 pages. An improved epidemic based on worm propagation communication radius and node distribution density is proposed This model uses the differential dynamic theory to analyze the dynamic process of worm propagation in wireless sensor networks. However, this model does not consider the differences of nodes, nor does it introduce link layer access conflicts and MAC avoidance mechanisms into the research.

[0010] Shen Shigen et al. A strategy to prevent malicious transmission in wireless sensor networks based on differential games [J]. IEEE Information Forensics and Security, 2014, 9(11): 1962-1972. An improved epidemic model was proposed, using differential game theory to When the malicious program spreads in the wireless sensor network, the decision-making problem between the wireless sensor network system and the malicious program is regarded as an optimal control problem. On the premise that the malicious program dynamically changes its strategy, the optimal control strategy of the wireless sensor network system is obtained, but this The model still does not consider the differences of nodes, and has limitations on the spatio-temporal characteristics of the network

[0011] In addition, there are three literatures on the propagation of malicious programs in mobile wireless sensor networks. Among them, Wang Xiaoming et al. Reaction Diffusion Model of Malware Propagation in Mobile Wireless Sensor Networks [J]. Information Science, 2013, 56:1-18. Proposed A reaction-diffusion equation-theoretical malware propagation model for mobile wireless sensor networks is proposed, which effectively predicts the time-dependent dynamic behavior and spatial distribution of malware propagation in order to facilitate targeted immunization at infected nodes. Measures, the model makes up for the existing malware propagation models that can only predict the temporal dynamic behavior, but cannot predict the time distribution of malware propagation;

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