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Dynamic model of Malware propagation based on tripartite graph and spread influence

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Abstract

The large-scale use of the Internet brings the problem of the rapid spread of computer malware over the network. Aiming at the relationship between malware, propagation paths and users in network propagation, this paper proposes a perception propagation model of computer malware based on a tripartite graph. First of all, aiming at the driving and influence of malware, propagation paths and users’ association in the network, this paper introduces a malware propagation tree structure, constructs two bipartite graphs of malware–propagation paths and propagation paths–users, and takes the paths as a bridge to form a tripartite graph of malware, propagation paths and users. Second, aiming at the complexity of the driving factors of malware in the process of propagation and the multiplicity of influence, by introducing a Cross-iteration Scoring mechanism of tripartite graph and influencing quantification algorithm, a method to measure the influence of malware propagation is proposed. At the same time, it uses multiple linear regression to uniformly quantify the impact. Finally, considering the polymorphism in the process of computer malware propagation, time slicing and infection state refinement are introduced. Based on the traditional propagation model, the infection state is divided into the normal infection state and the high infection state, and the tripartite iterative algorithm and the influence power method are comprehensively considered. A novel propagation dynamic model of malware is proposed. Experiments show that the model can not only discover the spread situation of malware in the network, but also explore the relationship between malware, propagation paths and users and their influence on the spread situation.

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Acknowledgements

This paper is partially supported by the National Natural Science Foundation of China (Grant No.61772098); Chongqing Graduate Education Teaching Reform Project(Grant No.yjg183081); Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.kjon201800641); Doctoral Top Talents Program of CQUPT, China (Grant No. BYJS2017004); and Chongqing Research Program of Application Foundation and Advanced Technology (cstc2019jcyj-msxmX0588).

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Tun Li, Yanbing Liu, Xinhong Wu, Yunpeng Xiao & Chunyan Sang

  2. Chongqing Engineering Lab Internet&Information Security, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Tun Li

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  1. Tun Li
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Correspondence to Tun Li.

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Li, T., Liu, Y., Wu, X. et al. Dynamic model of Malware propagation based on tripartite graph and spread influence. Nonlinear Dyn 101, 2671–2686 (2020). https://doi.org/10.1007/s11071-020-05935-6

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