【摘要】：網(wǎng)絡(luò)安全防護(hù)水平隨著網(wǎng)絡(luò)規(guī)模的不斷擴(kuò)大被提升到一個新的高度。傳統(tǒng)的入侵檢測均基于單條鏈路或者單個節(jié)點，但是在大規(guī)模網(wǎng)絡(luò)中大多數(shù)異常在單條鏈路或者單個節(jié)點的表征不明顯，這就導(dǎo)致傳統(tǒng)的入侵檢測不能兼顧到大規(guī)模網(wǎng)絡(luò)，，因此需要將云計算引入到入侵檢測中。Hadoop云計算平臺在海量數(shù)據(jù)處理上具有的高效、高容錯、高擴(kuò)展和高可靠性以及開源的特點均有利于提高海量數(shù)據(jù)的處理能力，因此將Hadoop云計算平臺引入入侵檢測勢在必行。 本文首先主要研究了Hadoop的兩個關(guān)鍵技術(shù)：HDFS存儲框架和MapReduce計算框架。分析并拆解了多尺度主成分分析(MSPCA)的主要步驟，并且基于MapReduce實現(xiàn)了對MSPCA算法的并行化；其次對并行化的MSPCA算法進(jìn)行了可擴(kuò)展性與加速比試驗；最后在原型系統(tǒng)中驗證并行化MSPCA算法對于異常流量的檢測能力。
[Abstract]:With the expansion of network scale, the level of network security protection has been upgraded to a new level. Traditional intrusion detection is based on a single link or a single node, but in a large-scale network, most of the anomalies are not obvious in a single link or a single node, which leads to the traditional intrusion detection can not take into account the large-scale network. Therefore, cloud computing needs to be introduced into intrusion detection. Hadoop cloud computing platform has the characteristics of high efficiency, high fault tolerance, high scalability, high reliability and open source, which are helpful to improve the processing capability of massive data. Therefore, it is imperative to introduce Hadoop cloud computing platform into intrusion detection. Firstly, two key technologies of Hadoop are studied: HDFS storage framework and MapReduce computing framework. The main steps of multi-scale principal component analysis (MSPCA) are analyzed and disassembled, and the parallelization of MSPCA algorithm is realized based on MapReduce. Secondly, the scalability and speedup test of parallel MSPCA algorithm are carried out. Finally, the ability of parallel MSPCA algorithm to detect abnormal traffic is verified in the prototype system.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.08

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本文編號：2470910