复杂网络的病毒传播模型研究与分析

发布时间：2018-07-06 18:11

本文选题：复杂网络 + 社交网络　；参考：《南京理工大学》2014年硕士论文

【摘要】：计算机技术的迅速发展已经使得计算机成为了人们生活中不可或缺的组成部分,但是计算机网络上的病毒传播也带给了人们巨大的损失。因此研究计算机病毒的传播机理,分析病毒传播的关键因素,为病毒的防治和相关政策的制定具有重要的现实意义。近些年复杂网络理论引起了人们的关注,学者们利用适当的复杂网络来描述现实生活中大量的生物、社会等复杂系统。利用复杂网络理论对计算机病毒在网络中进行传播模式和特征的研究也成为了学者们研究的热点。社交网络是一种新兴的复杂系统,和传统的复杂系统相比,在病毒传播的过程中用户行为因素起到了不可忽略的作用,统计数据和科学家们的研究表明,朴素的复杂网络理论很难描述社交网络上的病毒传播行为。论文在对针对复杂网络中病毒传播研究现状进行了广泛调研的基础上,通过结合现有的理论研究成果以及用户行为及社会工程学的相关理论,建立了在线社交网络中的病毒传播模型,并分析影响在线社交网络上病毒传播的关键因素。本论文的主要研究工作如下： 1.在线社交网络中,用户登录时间频率、用户好友数目以及网络中病毒初始感染率对社交网络上病毒传播的影响。本文通过数学分析和建模,提出了适用于描述在线社交网络上病毒传播的SEIR模型。 2.本文创新性地结合舆论传播学的理论,引入社会强化因子的概念来描述社交网络中的病毒传播过程。社会强化因子可以很好地描述在社交网络中用户从好友处收到若干次病毒信号才会接收信息,进而感染病毒的事件。本文提出了结合舆论传播学的社交网络上的SEIR病毒传播模型。在实验中,本文对比并深入分析了在规则网络和随机网络两种不同的拓扑结构中病毒的传播规律。本文通过分析实验结果,验证了上述提出的两类模型可以从不同侧面有效地模拟出社交网络中病毒传播的规律。在模型一中,本文的研究和实验分析表明,用户登录时间频率和用户好友数目这两个因素会显著增强社交网络中病毒的传播速率与传播范围,明显地增强了社交网络中病毒快速传播的危险性；在模型二中,本文通过实验,提出社会强化因子和网络初始病毒感染率在病毒传播过程中联合起到了非常重要的影响作用,本文对实验中的特殊情况进行了分析和说明；同时,通过统计分析,逼出了在社交网络中,用户第二次收到病毒信息时的感染概率最大,即提出了社会强化因子的阈值;最后通过分析实验结果,验证了提出的模型可以模拟社交网络中病毒传播的有效性。
[Abstract]:With the rapid development of computer technology, computers have become an indispensable part of people's lives, but the spread of viruses in computer networks has also brought great losses to people. Therefore, it is of great practical significance to study the transmission mechanism of computer virus and analyze the key factors of virus transmission for the prevention and control of virus and the formulation of relevant policies. In recent years, the theory of complex networks has attracted people's attention. Scholars use appropriate complex networks to describe a large number of biological, social and other complex systems in real life. Using complex network theory to study the transmission mode and characteristics of computer virus in the network has also become a hot topic for scholars. Social network is a new complex system. Compared with traditional complex system, user behavior plays an important role in virus transmission. Simple complex network theory is difficult to describe the spread of virus on social networks. On the basis of extensive investigation on the current situation of virus transmission in complex networks, this paper combines the existing theoretical research results and the relevant theories of user behavior and social engineering. The virus transmission model in online social network is established, and the key factors influencing virus transmission on online social network are analyzed. The main work of this thesis is as follows: 1. In online social networks, the frequency of users' login time, the number of users' friends and the initial infection rate of viruses in the network affect the spread of viruses on social networks. Based on mathematical analysis and modeling, a SEIR model is proposed to describe the spread of virus on online social networks. 2. Based on the theory of public opinion communication, this paper introduces the concept of social reinforcement factor to describe the process of virus transmission in social networks. Social Enhancement Factor can well describe the event in which a user receives a virus signal from a friend several times in a social network before he receives the message and then infects the virus. In this paper, a SEIR virus transmission model based on public opinion communication on social networks is proposed. In the experiment, we compare and analyze the transmission law of virus in two different topologies: regular network and random network. By analyzing the experimental results, it is verified that the two kinds of models mentioned above can effectively simulate the law of virus transmission in social networks from different aspects. In model one, the research and experimental analysis show that the frequency of user login time and the number of users' friends can significantly enhance the spread rate and spread range of the virus in social networks. In the second model, we propose that the social reinforcement factor and the initial infection rate of the network play a very important role in the process of virus transmission. This paper analyzes and explains the special situation in the experiment, at the same time, through statistical analysis, the author concludes that in the social network, the probability of infection is the greatest when the user receives the virus information for the second time, that is, the threshold value of social enhancement factor is put forward. Finally, the experimental results show that the proposed model can simulate the spread of virus in social networks.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.08;O157.5

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