發(fā)布時間：2019-06-28 12:57

【摘要】：隨著網(wǎng)絡(luò)技術(shù)的快速發(fā)展,計算機(jī)與互聯(lián)網(wǎng)給人類的生活帶來了翻天覆地的變化,它們在經(jīng)濟(jì)、文化等領(lǐng)域也發(fā)揮著舉足輕重的作用。與此同時計算機(jī)及網(wǎng)絡(luò)安全問題日益嚴(yán)峻,在這種背景下,入侵檢測成為關(guān)注的焦點。入侵檢測可以實時地對計算機(jī)系統(tǒng)進(jìn)行監(jiān)控,保證系統(tǒng)安全,近年來得到了廣泛的應(yīng)用。但入侵檢測系統(tǒng)占用了較高的計算機(jī)資源,如何提高系統(tǒng)性能一直是學(xué)者們研究的核心問題。本文主要利用貝葉斯網(wǎng)絡(luò)對入侵檢測展開研究。貝葉斯網(wǎng)絡(luò)作為一種強(qiáng)大的概率推理工具,它不僅降低了樸素貝葉斯對于屬性間條件獨(dú)立的要求,而且簡明地展示了屬性之間的依賴關(guān)系,降低了入侵檢測模型的復(fù)雜度。首先,本文分析了入侵檢測模型存在的主要問題,介紹了相關(guān)技術(shù)。之后對粗糙集理論及它在屬性約簡中的應(yīng)用做了詳細(xì)的分析;最后構(gòu)建了基于貝葉斯網(wǎng)絡(luò)的入侵檢測模型。針對不完備數(shù)據(jù)集,本文提出了R-BN算法。該算法以粗糙集中的分明矩陣為基礎(chǔ),找到數(shù)據(jù)集中與缺失對象最相似的對象,利用該對象屬性值對缺失對象進(jìn)行補(bǔ)齊。通過實驗比較了R-BN算法與常規(guī)補(bǔ)齊算法構(gòu)建的模型的分類效率,分類正確率得到了大幅提高。針對靜態(tài)模型在網(wǎng)絡(luò)環(huán)境發(fā)生改變時,影響分類效率的問題,本文提出了結(jié)構(gòu)動態(tài)變化的R-BN算法。該算法引入滑動窗口,將分類后的數(shù)據(jù)對象添加至數(shù)據(jù)集尾部,隨著窗口滑動實現(xiàn)數(shù)據(jù)的更新。當(dāng)網(wǎng)絡(luò)環(huán)境發(fā)生變化時,算法比較兩個窗口間的相對歐幾里得距離,判斷是否更新貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)與參數(shù),并通過實驗驗證了模型分類的正確性,相對于靜態(tài)模型,該模型的分類正確率得到了一定提高。
[Abstract]:With the rapid development of network technology, computer and Internet have brought earth-shaking changes to human life, and they also play an important role in economy, culture and other fields. At the same time, the problem of computer and network security is becoming more and more serious. In this context, intrusion detection has become the focus of attention. Intrusion detection can monitor the computer system in real time to ensure the security of the system, which has been widely used in recent years. However, intrusion detection system occupies high computer resources, how to improve the performance of the system has been the core issue of scholars. In this paper, Bayesian network is used to study intrusion detection. As a powerful probabilistic reasoning tool, Bayesian network not only reduces the requirement of naive Bays for conditional independence between attributes, but also succinctly shows the dependence between attributes and reduces the complexity of intrusion detection model. First of all, this paper analyzes the main problems of intrusion detection model, and introduces the related technologies. Then the rough set theory and its application in attribute reduction are analyzed in detail. Finally, an intrusion detection model based on Bayesian network is constructed. In this paper, a R-BN algorithm is proposed for incomplete data sets. Based on the clear matrix of rough set, the algorithm finds the object which is most similar to the missing object in the data set, and uses the attribute value of the object to complement the missing object. The classification efficiency of the model constructed by R-BN algorithm and conventional complement algorithm is compared by experiments, and the classification accuracy is greatly improved. In order to solve the problem that the static model affects the classification efficiency when the network environment changes, a R-BN algorithm with dynamic structural change is proposed in this paper. In this algorithm, sliding window is introduced, the classified data object is added to the tail of data set, and the data is updated with window sliding. When the network environment changes, the algorithm compares the relative Euclidean distance between the two windows, determines whether to update the Bayesian network structure and parameters, and verifies the correctness of the model classification through experiments. Compared with the static model, the classification accuracy of the model is improved to a certain extent.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.08

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本文編號：2507321