發(fā)布時(shí)間：2018-12-15 12:45

【摘要】：當(dāng)今社會(huì)，計(jì)算機(jī)網(wǎng)絡(luò)發(fā)展迅速，確保網(wǎng)絡(luò)信息的安全性就顯得尤為重要。能夠主動(dòng)保護(hù)信息安全的入侵檢測(cè)技術(shù)，作為一種保障措施而備受關(guān)注。神經(jīng)網(wǎng)絡(luò)的優(yōu)勢(shì)在于，它能夠作為一種方法應(yīng)用到入侵檢測(cè)中。通過分析和訓(xùn)練大量的實(shí)例數(shù)據(jù)，神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)訓(xùn)練的知識(shí)，根據(jù)已有的實(shí)例，自主掌握并分析出系統(tǒng)中各個(gè)實(shí)例和變量之間的關(guān)系，而不需要了解數(shù)據(jù)分布和解析的細(xì)節(jié)。 本文主要對(duì)入侵檢測(cè)的概念、功能以及檢測(cè)方法進(jìn)行詳細(xì)的介紹，并詳細(xì)闡述了神經(jīng)網(wǎng)絡(luò)的概念、工作原理以及神經(jīng)網(wǎng)絡(luò)的研究?jī)?nèi)容。重點(diǎn)介紹了BP算法的原理、步驟以及流程，根據(jù)BP神經(jīng)網(wǎng)絡(luò)模型的特點(diǎn)，通過比較算法的優(yōu)缺點(diǎn)，對(duì)現(xiàn)有算法進(jìn)行改進(jìn)。 首先，從神經(jīng)網(wǎng)絡(luò)的原理入手，對(duì)該原理進(jìn)行討論，研究了傳統(tǒng)BP網(wǎng)絡(luò)學(xué)習(xí)算法，并結(jié)合分布式和自適應(yīng)的特點(diǎn)，對(duì)傳統(tǒng)BP算法進(jìn)行改進(jìn)，提出了一種優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)入侵檢測(cè)算法，即分布式神經(jīng)網(wǎng)絡(luò)自學(xué)習(xí)算法。通過改進(jìn)的算法對(duì)入侵?jǐn)?shù)據(jù)進(jìn)行檢測(cè)和學(xué)習(xí)，直接使用BP學(xué)習(xí)方法的訓(xùn)練樣本數(shù)量過大而且不易收斂，這一問題得到了很好地解決。 其次，通過對(duì)改進(jìn)算法的研究，給出算法的具體步驟，并運(yùn)用改進(jìn)的算法來(lái)建立模型，對(duì)該模型進(jìn)行分析，與傳統(tǒng)BP網(wǎng)絡(luò)學(xué)習(xí)算法進(jìn)行對(duì)比，，驗(yàn)證改進(jìn)算法的可行性與有效性。 最后，將算法應(yīng)用于入侵檢測(cè)中，通過相應(yīng)的測(cè)試方法，對(duì)本文所采用的樣本數(shù)據(jù)集來(lái)進(jìn)行實(shí)例驗(yàn)證。通過檢測(cè)數(shù)據(jù)的測(cè)試結(jié)果，驗(yàn)證分布式神經(jīng)網(wǎng)絡(luò)自學(xué)習(xí)算法的性能，得出結(jié)論。
[Abstract]:Nowadays, with the rapid development of computer network, it is very important to ensure the security of network information. Intrusion detection technology, which can actively protect information security, has attracted much attention as a safeguard. The advantage of neural network is that it can be applied to intrusion detection as a method. By analyzing and training a large number of case data, neural networks learn the knowledge of training, according to the existing examples, independently grasp and analyze the relationship between each instance and variables in the system, without the need to understand the details of data distribution and analysis. In this paper, the concept, function and detection method of intrusion detection are introduced in detail, and the concept, working principle and research content of neural network are described in detail. The principle, steps and flow of BP algorithm are introduced emphatically. According to the characteristics of BP neural network model, the advantages and disadvantages of the algorithm are compared and the existing algorithms are improved. Firstly, this paper discusses the principle of neural network, studies the traditional learning algorithm of BP network, and combines the characteristics of distributed and adaptive, improves the traditional BP algorithm. An optimized BP neural network intrusion detection algorithm, distributed neural network self-learning algorithm, is proposed. By using the improved algorithm to detect and learn intrusion data, the number of training samples using BP learning method is too large and difficult to converge. This problem has been solved well. Secondly, through the research of the improved algorithm, the concrete steps of the algorithm are given, and the model is established by using the improved algorithm, and the model is analyzed, and compared with the traditional BP network learning algorithm, the feasibility and effectiveness of the improved algorithm are verified. Finally, the algorithm is applied to intrusion detection, and the sample data set used in this paper is verified by the corresponding test method. The performance of the distributed neural network self-learning algorithm is verified by the test results of the data, and the conclusion is drawn.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08;TP183

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 趙菁偉;基于分簇Ad Hoc網(wǎng)絡(luò)的入侵檢測(cè)系統(tǒng)設(shè)計(jì)[D];河北科技大學(xué);2016年

2 許鋒;人工神經(jīng)網(wǎng)絡(luò)與遺傳算法相結(jié)合的入侵檢測(cè)模型的研究[D];江蘇科技大學(xué);2015年

本文編號(hào)：2380651