本文選題：無標(biāo)度網(wǎng)絡(luò) + 惡意軟件擴(kuò)散��； 參考：《華中科技大學(xué)》2012年碩士論文

【摘要】：互聯(lián)網(wǎng)信息數(shù)量的急速增長，使得搜索引擎的作用越來越重要，進(jìn)而使得越來越多的惡意軟件選擇通過搜索引擎進(jìn)行傳播。本文通過研究惡意軟件經(jīng)由搜索引擎在網(wǎng)絡(luò)中進(jìn)行傳播的行為過程，建立了惡意軟件擴(kuò)散的多智能體仿真模型，并針對(duì)仿真模型得出的結(jié)果，提出了幾條具有參考性的控制策略。 首先，，本文針對(duì)用戶通過搜索引擎點(diǎn)擊網(wǎng)站后，主機(jī)在惡意軟件擴(kuò)散過程中所處的不同狀態(tài)，將復(fù)雜網(wǎng)絡(luò)中的主機(jī)分為五種：永久免疫主機(jī)、永久感染主機(jī)、易感染主機(jī)、暫時(shí)感染主機(jī)、暫時(shí)免疫主機(jī)。根據(jù)主機(jī)狀態(tài)變化過程和狀態(tài)變化影響因素，列出主機(jī)不同狀態(tài)變化的數(shù)學(xué)公式。 然后，本文將復(fù)雜網(wǎng)絡(luò)技術(shù)和多智能體技術(shù)相結(jié)合，根據(jù)文章中的數(shù)學(xué)公式，設(shè)計(jì)了惡意軟件在復(fù)雜網(wǎng)絡(luò)中傳播的多智能體仿真模型。該模型以無標(biāo)度網(wǎng)絡(luò)為研究環(huán)境，通過探討永久免疫主機(jī)的數(shù)量、永久惡意主機(jī)的數(shù)量、惡意軟件感染率、主機(jī)的學(xué)習(xí)能力和抵抗力、惡意軟件的進(jìn)化能力、惡意軟件的傳播方式、惡意軟件的傳播能力、用戶的“口碑”效應(yīng)等，來分析影響惡意軟件擴(kuò)散的相關(guān)因素。 隨后，根據(jù)惡意軟件傳播的仿真模擬結(jié)果和敏感性分析發(fā)現(xiàn)，永久免疫主機(jī)的數(shù)量、主機(jī)的學(xué)習(xí)能力對(duì)控制惡意軟件的傳播具有正相關(guān)性。永久惡意主機(jī)的數(shù)量、惡意軟件的進(jìn)化能力、惡意軟件的傳播率、用戶的“口碑”效應(yīng)對(duì)控制惡意軟件的傳播具有正相關(guān)性。惡意軟件的傳播方式對(duì)惡意軟件傳播也具有一定的影響。 最后，針對(duì)惡意軟件的利益鏈條、實(shí)驗(yàn)仿真結(jié)果和敏感性分析，提出幾條遏制惡意軟件傳播行之有效的方法。主要從政府、網(wǎng)站、法律、用戶等方面綜合考慮：政府方面要提高其監(jiān)管作用；網(wǎng)站方面搜索引擎要當(dāng)好網(wǎng)絡(luò)把關(guān)人，增加大型網(wǎng)站的數(shù)量，增加行業(yè)自律性；法律方面要制定完善的法律法規(guī)；用戶方面要養(yǎng)成良好上網(wǎng)習(xí)慣，提高主機(jī)免疫力。
[Abstract]:With the rapid growth of Internet information, the role of search engines becomes more and more important, and more malware are selected to spread through search engines. In this paper, a multi-agent simulation model of malware diffusion is established by studying the behavior of malicious software propagating through the network through search engine, and the results obtained by the simulation model are discussed. Several control strategies with reference are proposed. First of all, aiming at the different status of the host in the process of malware diffusion after the user clicks on the website through the search engine, this paper divides the host in the complex network into five kinds: permanent immune host, permanent infection host, vulnerable host. Temporary infection host, temporary immune host. According to the process of host state change and the influencing factors of state change, the mathematical formulas of host state change are listed. Then, this paper combines the complex network technology with the multi-agent technology, and designs a multi-agent simulation model of malware spread in the complex network according to the mathematical formula in this paper. The model takes scale-free network as the research environment, and discusses the number of permanent immune hosts, the number of permanent malicious hosts, the infection rate of malware, the learning ability and resistance of hosts, and the evolutionary ability of malicious software. The spread mode of malware, the spreading ability of malware, the word-of-mouth effect of users and so on, are analyzed to analyze the related factors that affect the spread of malware. Then, according to the simulation results and sensitivity analysis of malware propagation, it is found that the number of permanent immune hosts and the learning ability of hosts have a positive correlation to control the spread of malware. The number of permanent malware hosts, the evolutionary ability of malware, the spread rate of malware and the "word-of-mouth" effect of users have positive correlation to control the spread of malware. Malware communication mode also has certain influence on malware communication. Finally, according to the benefit chain of malware, the simulation results and sensitivity analysis, several effective methods to curb the spread of malware are put forward. Mainly from the government, website, law, users and other aspects of comprehensive consideration: the government should improve its regulatory role, website search engine should be a good network gatekeeper, increase the number of large websites, increase the self-discipline of the industry; The law should make perfect laws and regulations, and users should develop good internet habits and improve host immunity.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP393.08;TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 顧亦然;王鎖萍;;一種惡意軟件傳播的離散概率模型[J];電子學(xué)報(bào);2010年04期

2 佟賀豐;屈慰雙;劉婭;;中國鋼鐵行業(yè)CO_2排放的系統(tǒng)動(dòng)力學(xué)情景分析[J];高技術(shù)通訊;2010年05期

3 余前佳;;惡意軟件的特征、危害性及其防范與清除方法[J];國土資源信息化;2006年06期

4 高軍;肖衛(wèi);;試述內(nèi)網(wǎng)計(jì)算機(jī)病毒/惡意軟件的防范[J];長江工程職業(yè)技術(shù)學(xué)院學(xué)報(bào);2011年01期

5 楊賦立;品牌名稱在搜索引擎中的傳播[J];湖州職業(yè)技術(shù)學(xué)院學(xué)報(bào);2004年04期

6 姚燦中;楊建梅;;基于Multi-Agent的復(fù)雜網(wǎng)絡(luò)疾病傳播仿真模型[J];計(jì)算機(jī)工程與應(yīng)用;2010年20期

7 劉芳,何守才;搜索引擎技術(shù)的優(yōu)化處理方法[J];計(jì)算機(jī)工程;2003年16期

8 陳紅;聶曉峰;荊繼武;王躍武;;基于SEM的網(wǎng)絡(luò)蠕蟲傳播模型研究[J];計(jì)算機(jī)仿真;2010年08期

9 侯立文;徐建盈;;惡意軟件市場的系統(tǒng)動(dòng)力學(xué)仿真[J];計(jì)算機(jī)應(yīng)用研究;2010年10期

10 宋玉蓉;蔣國平;;無線傳感器網(wǎng)絡(luò)中惡意軟件傳播研究[J];南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年04期

相關(guān)碩士學(xué)位論文 前2條

1 張宏琳;網(wǎng)絡(luò)蠕蟲傳播過程的系統(tǒng)動(dòng)力學(xué)模型研究[D];陜西師范大學(xué);2007年

2 曹桂芳;多智能體動(dòng)態(tài)網(wǎng)絡(luò)系統(tǒng)中的病毒傳播機(jī)理研究[D];上海交通大學(xué);2009年

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