本文選題：Hadoop云平臺 + 分布式拒絕服務攻擊��； 參考：《山東科技大學》2017年碩士論文

【摘要】：DDoS (Distributed Denial of Service,分布式拒絕服務)攻擊是一種通過控制大量計算機(本文稱為傀儡機)發(fā)送超大數(shù)據(jù)量的資源請求來侵占應用資源、網(wǎng)絡帶寬資源以及系統(tǒng)資源的網(wǎng)絡攻擊,它以破壞計算機系統(tǒng)和網(wǎng)絡的可用性為目標,是目前威脅互聯(lián)網(wǎng)安全的最主要因素之一。目前已有一些較為成熟的單機DDoS攻擊檢測技術,但由于單臺計算機或服務器的檢測能力有限,目前已有的DDoS攻擊檢測技術很難有效的檢測針對大型局域網(wǎng)的DDoS攻擊。Hadoop云平臺將大規(guī)模存儲與計算資源進行有效整合,通過眾多計算機以集群的方式并行運行,實現(xiàn)高速計算和存儲。本文通過在單機環(huán)境下DDoS攻擊檢測較為成熟的SVM算法結合Hadoop云平臺高速計算分析能力和強大的存儲能力,有效的解決大型局域網(wǎng)的DDoS攻擊的檢測問題。本文首先在研究DDoS攻擊的原理的基礎上對DDoS攻擊的類型進行分類,然后研究Hadoop相關技術以及單機SVM算法,將SVM算法推廣到Hadoop云平臺下,設計了基于Hadoop環(huán)境下的并行分布式SVM算法。該算法通過對訓練樣本合理分塊,在層疊訓練過程中設置合理的層疊停止條件以及自定義MapReduce過程,解決在訓練學習過程中訓練樣本隨機分布導致得到分類器不準確或者出現(xiàn)得不到分類器的極端情況問題,以及將訓練樣本劃分成小樣本塊進行層疊訓練過程中的重新散列等問題,充分利用Hadoop云平臺高速計算分析能力和強大的存儲能力,在保證準確率的同時提高訓練學習效率。本文同時提出 DDBHS (DDoS Attack Detection Based on Hadoop and SVM)系統(tǒng)的概念,利用基于Hadoop云平臺的并行分布式SVM算法進行訓練學習并檢測DDoS攻擊,對DDBHS系統(tǒng)的檢測模塊和Hadoop云平臺的系統(tǒng)結構進行了設計,通過建立攻擊檢測聯(lián)盟,使控制節(jié)點可以控制訓練學習節(jié)點和攻擊檢測節(jié)點的狀態(tài)和責任轉換,在提升檢測效率的同時有效利用系統(tǒng)資源。論文實現(xiàn)了 DDBHS系統(tǒng),并將其應用到實際環(huán)境中進行DDoS攻擊檢測,通過實測驗證了論文所設計實現(xiàn)的DDBHS系統(tǒng)針對DDoS攻擊具有較高的效率和準確性。
[Abstract]:DDoS distributed Denial of Service (DDoS) attack is a network attack that invades application resources, network bandwidth resources and system resources by controlling a large number of computers (in this paper called puppet machines) to send large amount of data resource requests.It aims to destroy the usability of computer systems and networks and is one of the most important factors threatening Internet security.At present, there are some mature single machine DDoS attack detection techniques, but the detection ability of single computer or server is limited.At present, the existing DDoS attack detection technology is very difficult to effectively detect the DDoS attack. Hadoop cloud platform against large local area networks, which integrates large-scale storage and computing resources effectively, and runs in parallel through numerous computers in a cluster manner.Achieve high speed computing and storage.In this paper, the problem of DDoS attack detection in large local area network is effectively solved by using the mature SVM algorithm of DDoS attack detection in a single computer environment, combined with the high speed computing and analyzing ability and powerful storage ability of Hadoop cloud platform.In this paper, the types of DDoS attacks are classified on the basis of studying the principle of DDoS attacks, and then the related techniques of Hadoop and single-machine SVM algorithms are studied, and the SVM algorithm is extended to the Hadoop cloud platform.A parallel distributed SVM algorithm based on Hadoop is designed.By dividing the training samples into blocks reasonably, the algorithm sets reasonable stack stopping conditions and custom MapReduce process in the process of cascading training.In order to solve the problem that the random distribution of training samples leads to inaccurate classifier or the extreme situation of not getting classifier in the process of training and learning,The training samples are divided into small sample blocks and rehashing in the process of cascading training, which makes full use of the high speed computing and analysis ability and powerful storage ability of Hadoop cloud platform, so as to ensure the accuracy of training and learning efficiency at the same time.At the same time, the concept of DDBHS DDoS Attack Detection Based on Hadoop and SVM system is proposed. The parallel distributed SVM algorithm based on Hadoop cloud platform is used to train and learn and detect DDoS attacks. The detection module of DDBHS system and the system structure of Hadoop cloud platform are designed.By establishing the attack detection alliance, the control node can control the state and responsibility transition between the training learning node and the attack detection node, and make effective use of the system resources while improving the detection efficiency.In this paper, the DDBHS system is implemented, and it is applied to the real environment to detect the DDoS attack. The experimental results show that the DDBHS system designed and implemented in this paper has high efficiency and accuracy for DDoS attack.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.08

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