本文選題：數(shù)據(jù)挖掘 + 異常流量檢測(cè)�。� 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：網(wǎng)絡(luò)自從誕生以來就因其無可替代的優(yōu)勢(shì)而在全球迅猛發(fā)展，如今已遍布生產(chǎn)、生活、服務(wù)、教育等人們社會(huì)活動(dòng)的方方面面，就如同空氣和水一樣無形而又無處不在，在給與人們方便的同時(shí)也深刻影響著人們的活動(dòng)習(xí)慣。 TCP/IP協(xié)議不是一個(gè)完美的產(chǎn)品，在病毒和黑客入侵遍布網(wǎng)絡(luò)的環(huán)境下，TCP/IP協(xié)議的漏洞使得沒有哪個(gè)企業(yè)可以幸免。一旦受到攻擊，網(wǎng)絡(luò)越發(fā)達(dá)的企業(yè)遭受的損失越慘重。所以如今保護(hù)本企業(yè)網(wǎng)絡(luò)成了各公司網(wǎng)絡(luò)管理人員的首要任務(wù)，對(duì)網(wǎng)絡(luò)防護(hù)技術(shù)的研究也是方興未艾，一日千里。 本文正是在這樣的環(huán)境下開展了對(duì)網(wǎng)絡(luò)防護(hù)的研究工作，集中在使用數(shù)據(jù)挖掘技術(shù)對(duì)企業(yè)局域網(wǎng)的異常流量的分析與檢測(cè)上。在對(duì)企業(yè)網(wǎng)絡(luò)中病毒感染、黑客入侵、設(shè)備故障等導(dǎo)致的異常流量進(jìn)行了分析的基礎(chǔ)上，研究異常流量的屬性選擇，，提出使用統(tǒng)計(jì)及聚類算法對(duì)異常流量進(jìn)行分級(jí)檢測(cè)的方法，并使用異常模型對(duì)其進(jìn)行分類。進(jìn)一步提出一個(gè)企業(yè)異常流量檢測(cè)分類系統(tǒng)，該系統(tǒng)能夠監(jiān)控網(wǎng)絡(luò)，偵測(cè)到異常流量并進(jìn)行識(shí)別，能協(xié)助對(duì)網(wǎng)絡(luò)進(jìn)行自動(dòng)調(diào)整。最后，在企業(yè)實(shí)際網(wǎng)絡(luò)環(huán)境中對(duì)該系統(tǒng)進(jìn)行了測(cè)試，系統(tǒng)對(duì)異常流量檢測(cè)精度達(dá)到90%。
[Abstract]:Since its birth, the Internet has developed rapidly in the world because of its irreplaceable advantages. Now it has spread all aspects of people's social activities, such as production, life, service, education and so on, just as air and water are invisible and ubiquitous.While giving people convenience, it also deeply affects people's activity habits.The TCP/IP protocol is not a perfect product, and no enterprise is immune from the vulnerability of the TCP / IP protocol in the presence of viruses and hackers invading all over the network.Once attacked, the more developed the network of enterprises to suffer more heavy losses.Therefore, the protection of the enterprise network has become the first task of network managers, and the research of network protection technology is in the ascendant.In this paper, the research work of network protection is carried out in this environment, which focuses on the analysis and detection of abnormal traffic in enterprise LAN by using data mining technology.Based on the analysis of abnormal traffic caused by virus infection, hacker intrusion and equipment failure in enterprise network, this paper studies the attribute selection of abnormal traffic, and puts forward a method of classifying abnormal traffic by using statistical and clustering algorithms.The abnormal model is used to classify it.Furthermore, an enterprise anomaly traffic detection and classification system is proposed, which can monitor the network, detect and identify the abnormal traffic, and help to adjust the network automatically.Finally, the system is tested in the actual network environment, and the accuracy of the system is 90%.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06;TP311.13

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