计算机应用 ›› 2018, Vol. 38 ›› Issue (10): 2899-2902.DOI: 10.11772/j.issn.1001-9081.2018040850

• 网络空间安全 • 上一篇    下一篇

考虑社交网络用户行为的网络病毒传播建模

冯丽萍1,2,3, 韩燮1, 韩琦4, 郑芳2   

  1. 1. 中北大学 信息与通信工程学院, 太原 030051;
    2. 山西财经大学 信息管理学院, 太原 030006;
    3. 忻州师范学院 计算机系, 忻州 034000;
    4. 重庆科技学院 电气与信息工程学院, 重庆 401331
  • 收稿日期:2018-04-25 修回日期:2018-06-11 出版日期:2018-10-10 发布日期:2018-10-13
  • 通讯作者: 冯丽萍
  • 作者简介:冯丽萍(1976-),女,山西宁武人,教授,博士,CCF会员,主要研究方向:网络安全、动力系统;韩燮(1964-),女,山西文水人,教授,博士生导师,博士,主要研究方向:虚拟现实、网络安全;韩琦(1981-),男,山西榆社人,副教授,博士,主要研究方向:网络安全、优化控制、细胞神经网络;郑芳(1982-),女,山西石楼人,讲师,硕士,主要研究方向:信息安全、隐私保护。
  • 基金资助:
    国家自然科学基金资助项目(61503050);忻州师范学院重点学科建设项目(XK201403)。

Network virus propagation modeling considering social network user behaviors

FENG Liping1,2,3, HAN Xie1, HAN Qi4, ZHENG Fang2   

  1. 1. School of Information and Communication Engineering, North University of China, Taiyuan Shanxi 030051, China;
    2. School of Information Management, Shanxi University of Finance & Economics, Taiyuan Shanxi 030006, China;
    3. Computer Department, Xinzhou Teachers University, Xinzhou Shanxi 034000, China;
    4. School of Electrical and Information Engineering, Chongqing University of Science & Technology, Chongqing 401331, China
  • Received:2018-04-25 Revised:2018-06-11 Online:2018-10-10 Published:2018-10-13
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61503050), the Key Disciplines Construction Project of Xinzhou Teachers University (XK201403).

摘要: 针对已有病毒传播模型都没有考虑不同社交网络间的用户交互行为对网络病毒传播规律的影响,建立了考虑不同社交网络用户交互行为的微分方程动力学模型。利用稳定性理论分析了模型反映的网络病毒传播动力学性态,得到了控制网络病毒传播的基本再生数的精确数学表达式。进一步,采用龙格-库塔数值方法,通过仿真实验,验证了理论分析的正确性。研究结果表明,基本再生数是网络病毒扩散基本态势的直接决定因素,当基本再生数的值小于等于1时,随着时间演化,网络病毒的扩散会被彻底控制。另外还发现,分散用户到不同社交网络更有利于缓解网络病毒的扩散。

关键词: 病毒模型, 社交网络, 用户行为, 动力学性态, 网络病毒

Abstract: Concerning that the existing networks virus propagation models do not consider the influence of interactive behaviors among the users in different social networks on network virus propagation, a dynamic model of differential equations was established. The stability theory was used to analyze the dynamical behaviors of the network virus propagation, and the accurate expression of the basic reproduction number was obtained, which is the threshold of controlling the network virus propagation. Furthermore, using Runge-Kutta numerical method, the correctness of theoretic analysis was verified by simulations. The results show that the basic reproduction number is the direct decisive factor of network virus prevalence situations. When the value of the basic reproduction number is less than or equal to one, the propagation of the network viruses will be controlled with the evolution of time. Additionally, the research reveals that it is helpful for distributing the users to different social networks to slow the prevalence of network viruses.

Key words: virus model, social network, user behavior, dynamical behavior, network virus

中图分类号: