本文選題：防火墻 + 入侵檢測(cè)��； 參考：《蘭州交通大學(xué)》2014年碩士論文

【摘要】：指揮自動(dòng)化系統(tǒng)作為兵力的倍增器，在復(fù)雜多變的戰(zhàn)場(chǎng)環(huán)境中影響著戰(zhàn)斗的進(jìn)程。指揮自動(dòng)化網(wǎng)絡(luò)作為指揮自動(dòng)化系統(tǒng)功能得以實(shí)現(xiàn)的基礎(chǔ)，承擔(dān)著軍事信息數(shù)據(jù)的傳輸任務(wù)，在指揮自動(dòng)化系統(tǒng)得以發(fā)揮戰(zhàn)斗力的過程中起著至關(guān)重要的作用。指揮自動(dòng)化網(wǎng)絡(luò)的安全顯得尤為重要，針對(duì)當(dāng)前指揮自動(dòng)化網(wǎng)絡(luò)面臨的各種威脅，我軍采取防火墻、入侵檢測(cè)等網(wǎng)絡(luò)安全防御措施。針對(duì)傳統(tǒng)BP(Back Propagation,反向傳播)算法應(yīng)用于防火墻與入侵檢測(cè)中，存在收斂速度慢、易陷入局部極小值等缺陷。在傳統(tǒng)BP算法基礎(chǔ)上進(jìn)行改進(jìn)，將小波神經(jīng)網(wǎng)絡(luò)以及LM(Levenberg-Marquardt,列文伯格—馬夸爾特)算法分別應(yīng)用于指揮自動(dòng)化網(wǎng)絡(luò)防火墻流量的預(yù)測(cè)和入侵檢測(cè)的分類中。仿真結(jié)果從收斂速度、預(yù)測(cè)誤差、分類效果等方面可以看出，改進(jìn)算法應(yīng)用于防火墻流量預(yù)測(cè)與入侵分類中是十分有效的。具體研究?jī)?nèi)容如下： 首先，分別對(duì)防火墻流量的預(yù)測(cè)以及入侵檢測(cè)進(jìn)行建模。根據(jù)指揮自動(dòng)化網(wǎng)絡(luò)某防火墻流量的特點(diǎn)，研究防火墻流量的影響因素，包括當(dāng)前防火墻流量數(shù)據(jù)，星期以及時(shí)段等。確定防火墻流量預(yù)測(cè)輸入輸出之間的關(guān)系，對(duì)防火墻流量預(yù)測(cè)進(jìn)行建模。入侵檢測(cè)是防火墻的補(bǔ)充，是一種主動(dòng)的防御措施。對(duì)一些通過偽裝等方式進(jìn)行訪問的連接，防火墻無(wú)法辨別并進(jìn)行正確攔截的情況，入侵檢測(cè)通過主動(dòng)搜集影響指揮自動(dòng)化網(wǎng)絡(luò)關(guān)鍵節(jié)點(diǎn)的數(shù)據(jù)信息，將其作為神經(jīng)網(wǎng)絡(luò)的輸入。采用KDD CUP99數(shù)據(jù)集對(duì)指揮自動(dòng)化網(wǎng)絡(luò)進(jìn)行建模，得出當(dāng)前指揮自動(dòng)化網(wǎng)絡(luò)是否安全并將入侵進(jìn)行分類。 其次，分別對(duì)防火墻流量預(yù)測(cè)與入侵檢測(cè)算法進(jìn)行優(yōu)化。神經(jīng)網(wǎng)絡(luò)算法一個(gè)重要的應(yīng)用領(lǐng)域是預(yù)測(cè)問題。根據(jù)小波算法在時(shí)間序列預(yù)測(cè)問題中效果較好，將其應(yīng)用于指揮自動(dòng)化網(wǎng)絡(luò)防火墻流量的預(yù)測(cè)中。通過仿真分析，，從收斂速度和預(yù)測(cè)誤差的方面進(jìn)行對(duì)比分析；神經(jīng)網(wǎng)絡(luò)算法的另外一個(gè)重要的應(yīng)用領(lǐng)域是解決分類問題。針對(duì)傳統(tǒng)BP算法分類中存在的缺陷，將LM算法應(yīng)用于指揮自動(dòng)化網(wǎng)絡(luò)入侵分類中。 再次，借助Matlab實(shí)驗(yàn)平臺(tái)進(jìn)行仿真，對(duì)參數(shù)進(jìn)行調(diào)節(jié)，得出最優(yōu)解。分別對(duì)小波與傳統(tǒng)BP算法、LM與傳統(tǒng)BP算法進(jìn)行分析、比較。 最后，總結(jié)指揮自動(dòng)化網(wǎng)絡(luò)防火墻流量的預(yù)測(cè)以及入侵檢測(cè)中不同算法表現(xiàn)出的優(yōu)缺點(diǎn)，立足于指揮自動(dòng)化網(wǎng)絡(luò)面臨的實(shí)際情況，提出有待進(jìn)一步解決的問題。
[Abstract]:As a multiplier of forces, the command automation system affects the process of combat in the complex and changeable battlefield environment. As the basis for the realization of the functions of the command automation system, the command automation network undertakes the task of transmitting military information data, and plays an important role in the process of the command automation system playing a vital role in the process of exerting the combat effectiveness of the command automation system. The security of the command automation network is particularly important. In view of the various threats facing the command automation network, our army adopts network security defense measures such as firewall, intrusion detection and so on. The traditional BP(Back Propagation (back Propagation) algorithm used in firewall and intrusion detection has some shortcomings such as slow convergence rate and easy to fall into local minimum. Based on the traditional BP algorithm, wavelet neural network and Levenberg-Marquardt, Levenberg-Marquardt (Levenberg-Marquardt) algorithm are applied to the traffic prediction and intrusion detection classification of the command automation network firewall, respectively. The simulation results show that the improved algorithm is very effective in firewall traffic prediction and intrusion classification from the aspects of convergence speed, prediction error, classification effect and so on. The specific contents of the study are as follows: Firstly, the prediction of firewall traffic and intrusion detection are modeled. According to the characteristics of firewall traffic in command automation network, the influence factors of firewall traffic are studied, including the current firewall traffic data, week and time period and so on. The relationship between firewall traffic prediction input and output is determined, and firewall traffic prediction is modeled. Intrusion detection is a supplement to firewall and an active defense measure. For some connections which are accessed by camouflage, the firewall can not distinguish and intercept correctly. Intrusion detection takes it as the input of neural network by actively collecting the data information that affects the key nodes of the command automation network. The KDD CUP99 data set is used to model the command automation network, and the security of the current command automation network is obtained and the intrusion is classified. Secondly, the firewall traffic prediction and intrusion detection algorithm are optimized. An important application field of neural network algorithm is the prediction problem. According to the good effect of wavelet algorithm in time series prediction, it is applied to the prediction of firewall traffic in command automation network. Through simulation analysis, the convergence rate and prediction error are compared and analyzed. Another important application field of neural network algorithm is to solve the classification problem. Aiming at the shortcomings of the traditional BP algorithm, LM algorithm is applied to the intrusion classification of command automation network. Thirdly, with the help of Matlab experiment platform, the parameters are adjusted and the optimal solution is obtained. Wavelet and traditional BP algorithm LM and traditional BP algorithm are analyzed and compared. Finally, the paper summarizes the prediction of firewall traffic in command automation network and the advantages and disadvantages of different algorithms in intrusion detection. Based on the actual situation of command automation network, the problems that need to be solved further are put forward.
【學(xué)位授予單位】：蘭州交通大學(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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