發(fā)布時(shí)間：2018-07-06 18:11

  本文選題：復(fù)雜網(wǎng)絡(luò) + 社交網(wǎng)絡(luò)　； 參考：《南京理工大學(xué)》2014年碩士論文

【摘要】：計(jì)算機(jī)技術(shù)的迅速發(fā)展已經(jīng)使得計(jì)算機(jī)成為了人們生活中不可或缺的組成部分,但是計(jì)算機(jī)網(wǎng)絡(luò)上的病毒傳播也帶給了人們巨大的損失。因此研究計(jì)算機(jī)病毒的傳播機(jī)理,分析病毒傳播的關(guān)鍵因素,為病毒的防治和相關(guān)政策的制定具有重要的現(xiàn)實(shí)意義。 近些年復(fù)雜網(wǎng)絡(luò)理論引起了人們的關(guān)注,學(xué)者們利用適當(dāng)?shù)膹?fù)雜網(wǎng)絡(luò)來描述現(xiàn)實(shí)生活中大量的生物、社會(huì)等復(fù)雜系統(tǒng)。利用復(fù)雜網(wǎng)絡(luò)理論對(duì)計(jì)算機(jī)病毒在網(wǎng)絡(luò)中進(jìn)行傳播模式和特征的研究也成為了學(xué)者們研究的熱點(diǎn)。社交網(wǎng)絡(luò)是一種新興的復(fù)雜系統(tǒng),和傳統(tǒng)的復(fù)雜系統(tǒng)相比,在病毒傳播的過程中用戶行為因素起到了不可忽略的作用,統(tǒng)計(jì)數(shù)據(jù)和科學(xué)家們的研究表明,樸素的復(fù)雜網(wǎng)絡(luò)理論很難描述社交網(wǎng)絡(luò)上的病毒傳播行為。論文在對(duì)針對(duì)復(fù)雜網(wǎng)絡(luò)中病毒傳播研究現(xiàn)狀進(jìn)行了廣泛調(diào)研的基礎(chǔ)上,通過結(jié)合現(xiàn)有的理論研究成果以及用戶行為及社會(huì)工程學(xué)的相關(guān)理論,建立了在線社交網(wǎng)絡(luò)中的病毒傳播模型,并分析影響在線社交網(wǎng)絡(luò)上病毒傳播的關(guān)鍵因素。 本論文的主要研究工作如下： 1.在線社交網(wǎng)絡(luò)中,用戶登錄時(shí)間頻率、用戶好友數(shù)目以及網(wǎng)絡(luò)中病毒初始感染率對(duì)社交網(wǎng)絡(luò)上病毒傳播的影響。本文通過數(shù)學(xué)分析和建模,提出了適用于描述在線社交網(wǎng)絡(luò)上病毒傳播的SEIR模型。 2.本文創(chuàng)新性地結(jié)合輿論傳播學(xué)的理論,引入社會(huì)強(qiáng)化因子的概念來描述社交網(wǎng)絡(luò)中的病毒傳播過程。社會(huì)強(qiáng)化因子可以很好地描述在社交網(wǎng)絡(luò)中用戶從好友處收到若干次病毒信號(hào)才會(huì)接收信息,進(jìn)而感染病毒的事件。本文提出了結(jié)合輿論傳播學(xué)的社交網(wǎng)絡(luò)上的SEIR病毒傳播模型。在實(shí)驗(yàn)中,本文對(duì)比并深入分析了在規(guī)則網(wǎng)絡(luò)和隨機(jī)網(wǎng)絡(luò)兩種不同的拓?fù)浣Y(jié)構(gòu)中病毒的傳播規(guī)律。 本文通過分析實(shí)驗(yàn)結(jié)果,驗(yàn)證了上述提出的兩類模型可以從不同側(cè)面有效地模擬出社交網(wǎng)絡(luò)中病毒傳播的規(guī)律。在模型一中,本文的研究和實(shí)驗(yàn)分析表明,用戶登錄時(shí)間頻率和用戶好友數(shù)目這兩個(gè)因素會(huì)顯著增強(qiáng)社交網(wǎng)絡(luò)中病毒的傳播速率與傳播范圍,明顯地增強(qiáng)了社交網(wǎng)絡(luò)中病毒快速傳播的危險(xiǎn)性；在模型二中,本文通過實(shí)驗(yàn),提出社會(huì)強(qiáng)化因子和網(wǎng)絡(luò)初始病毒感染率在病毒傳播過程中聯(lián)合起到了非常重要的影響作用,本文對(duì)實(shí)驗(yàn)中的特殊情況進(jìn)行了分析和說明；同時(shí),通過統(tǒng)計(jì)分析,逼出了在社交網(wǎng)絡(luò)中,用戶第二次收到病毒信息時(shí)的感染概率最大,即提出了社會(huì)強(qiáng)化因子的閾值;最后通過分析實(shí)驗(yàn)結(jié)果,驗(yàn)證了提出的模型可以模擬社交網(wǎng)絡(luò)中病毒傳播的有效性。
[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.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08;O157.5

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本文編號(hào)：2103672