【摘要】：互聯(lián)網(wǎng)的出現(xiàn)，不僅給人們提供了一個(gè)良好的互相交流學(xué)習(xí)的平臺(tái)，拉近了人與人之間的距離，同時(shí)也把大量的威脅帶到我們身邊，黑客們通過(guò)各種手段破壞信息系統(tǒng)的安全。如何有效的保障信息的完整性，可用性和隱秘性，成為各個(gè)行業(yè)共同面對(duì)的挑戰(zhàn)。入侵檢測(cè)技術(shù)作為一種主動(dòng)防御技術(shù)，在保障網(wǎng)絡(luò)信息系統(tǒng)安全方面取得了重要成就。但是隨著信息社會(huì)進(jìn)入大數(shù)據(jù)時(shí)代，信息由原來(lái)的單一屬性或低維屬性向高維屬性轉(zhuǎn)化，數(shù)據(jù)呈現(xiàn)出大規(guī)模，呈現(xiàn)高維度、高噪聲、高復(fù)雜度的特點(diǎn)，造成“維度災(zāi)難”現(xiàn)象。為傳統(tǒng)的入侵檢測(cè)系統(tǒng)的計(jì)算能力及實(shí)時(shí)性需求提出了嚴(yán)峻的挑戰(zhàn)。 異常檢測(cè)作為入侵檢測(cè)的一種重要技術(shù)手段，因?yàn)榭梢詫?duì)未知攻擊進(jìn)行檢測(cè)而被受關(guān)注，支持向量機(jī)(SVM)作為典型有效的異常檢測(cè)算法，具有較好的分類效果。但樣本數(shù)據(jù)高維的特性和噪聲數(shù)據(jù)，嚴(yán)重的影響了SVM的分類效率。為了對(duì)檢測(cè)模型進(jìn)行優(yōu)化，降低復(fù)雜度，本文的研究工作針對(duì)ReliefF特征選擇算法展開(kāi)，對(duì)特征選擇的基本概念，典型步驟，應(yīng)用研究現(xiàn)狀等進(jìn)行了介紹。并對(duì)特征選擇和入侵檢測(cè)的研究現(xiàn)狀和基本概念進(jìn)行了簡(jiǎn)單介紹，包括對(duì)入侵檢測(cè)模型和基本流程的說(shuō)明，并對(duì)入侵檢測(cè)進(jìn)行歸納和分類。同時(shí)根據(jù)入侵檢測(cè)存在的缺點(diǎn)，介紹了未來(lái)入侵檢測(cè)的發(fā)展方向。 論文研究了ReliefF特征選擇算法及其在入侵檢測(cè)領(lǐng)域的應(yīng)用，，提出應(yīng)用方法和入侵特征的映射關(guān)系，并結(jié)合入侵檢測(cè)數(shù)據(jù)集中網(wǎng)絡(luò)數(shù)據(jù)相似高的特性，進(jìn)一步提出了針對(duì)傳統(tǒng)ReliefF應(yīng)用于入侵檢測(cè)領(lǐng)域的改進(jìn)算法Re-ReliefF，改進(jìn)主要針對(duì)特征權(quán)重計(jì)算方法結(jié)合入侵檢測(cè)數(shù)據(jù)特征進(jìn)行了優(yōu)化。 為了獲得更好的時(shí)間復(fù)雜度檢測(cè)效果，文中應(yīng)用特征選擇算法對(duì)SVM分類算法的數(shù)據(jù)進(jìn)行選擇處理，實(shí)驗(yàn)結(jié)果顯示，改進(jìn)后的Re-ReliefF算法在性能各方面相對(duì)于ReliefF算法都有所提高，經(jīng)過(guò)Re-ReliefF算法處理后的數(shù)據(jù)集，對(duì)于SVM的分類效果（即檢測(cè)率）影響不大，卻可節(jié)省大量檢測(cè)時(shí)間。
[Abstract]:The emergence of the Internet not only provides a good platform for people to communicate and learn from each other, but also brings a large number of threats to us. Hackers undermine the security of information systems through various means. How to effectively protect the integrity, availability and privacy of information has become a common challenge for all industries. As an active defense technology, intrusion detection technology has made important achievements in ensuring the security of network information system. However, as the information society enters the era of big data, the information is transformed from the original single attribute or the low-dimensional attribute to the high-dimensional attribute, and the data presents the characteristics of large scale, high dimension, high noise and high complexity, resulting in the phenomenon of "dimensionality disaster". It presents a severe challenge to the computing power and real-time requirement of the traditional intrusion detection system. As an important technique of intrusion detection, anomaly detection has attracted much attention because of its ability to detect unknown attacks. As a typical and effective anomaly detection algorithm, support vector machine (SVM) has a good classification effect. However, the high dimensional characteristics of sample data and noise data seriously affect the classification efficiency of SVM. In order to optimize the detection model and reduce the complexity, the research work in this paper is focused on ReliefF feature selection algorithm. The basic concept, typical steps and application status of feature selection are introduced. The research status and basic concepts of feature selection and intrusion detection are briefly introduced, including the description of intrusion detection model and basic process, and the induction and classification of intrusion detection. At the same time, according to the shortcomings of intrusion detection, the development direction of intrusion detection in the future is introduced. This paper studies ReliefF feature selection algorithm and its application in the field of intrusion detection, proposes the mapping relationship between application method and intrusion feature, and combines the characteristics of high similarity of network data in intrusion detection data set. Furthermore, an improved algorithm Re-ReliefFis for traditional ReliefF application in intrusion detection is proposed. The improved algorithm is mainly aimed at the feature weight calculation method combined with intrusion detection data feature optimization. In order to obtain better time complexity detection effect, the feature selection algorithm is used to select and process the data of SVM classification algorithm. The experimental results show that the improved Re-ReliefF algorithm improves the performance of the improved Re-ReliefF algorithm compared with the ReliefF algorithm. The data set processed by Re-ReliefF algorithm has little effect on the classification effect of SVM (that is, detection rate), but it can save a lot of detection time.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

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