【摘要】：隨著網(wǎng)絡(luò)規(guī)模的日趨龐大,結(jié)構(gòu)的日益復(fù)雜和多變,傳統(tǒng)的解決單個(gè)網(wǎng)絡(luò)安全問題的方法已經(jīng)無法滿足需求。對(duì)網(wǎng)絡(luò)的整體運(yùn)行情況進(jìn)行感知和預(yù)測(cè),已經(jīng)逐漸成為當(dāng)前網(wǎng)絡(luò)安全領(lǐng)域的研究熱點(diǎn)之一。網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)作為網(wǎng)絡(luò)安全態(tài)勢(shì)感知的重要內(nèi)容,使網(wǎng)絡(luò)安全管理從被動(dòng)變?yōu)橹鲃?dòng)。目前網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)僅僅利用當(dāng)前和過去的網(wǎng)絡(luò)安全態(tài)勢(shì)值對(duì)未來一段時(shí)間進(jìn)行預(yù)測(cè),這種方法預(yù)測(cè)數(shù)據(jù)單一,未結(jié)合各種環(huán)境影響因素。針對(duì)以上問題,本文主要工作和創(chuàng)新點(diǎn)如下:(1)深入研究了影響網(wǎng)絡(luò)安全態(tài)勢(shì)的各種環(huán)境因素。由于影響網(wǎng)絡(luò)安全態(tài)勢(shì)的環(huán)境因素較多,為了權(quán)衡預(yù)測(cè)的精度和效率,本文利用灰關(guān)聯(lián)熵分析方法選出與網(wǎng)絡(luò)安全態(tài)勢(shì)關(guān)聯(lián)程度較大的影響因素,并給出了完整的基于灰關(guān)聯(lián)熵和Kalman的網(wǎng)絡(luò)安全態(tài)勢(shì)感知模型。(2)提出了GRE-Kalman預(yù)測(cè)算法。結(jié)合選出的網(wǎng)絡(luò)安全態(tài)勢(shì)的關(guān)鍵因素,提出了基于灰關(guān)聯(lián)熵的Kalman預(yù)測(cè)算法(GRE-Kalman)。GRE-Kalman預(yù)測(cè)模型適用于任意個(gè)影響因素,可根據(jù)需要確定影響因素的個(gè)數(shù)。通過結(jié)合影響因素進(jìn)行預(yù)測(cè),提高了預(yù)測(cè)的精度和算法的適應(yīng)性。(3)提出了AP-Kalman預(yù)測(cè)算法。結(jié)合灰關(guān)聯(lián)熵分析方法選出的關(guān)鍵因素攻擊強(qiáng)度,分別利用前一個(gè)時(shí)間段的攻擊強(qiáng)度、前二個(gè)時(shí)間段的攻擊強(qiáng)度、前三個(gè)時(shí)間段的攻擊強(qiáng)度、前一個(gè)時(shí)間段的攻擊強(qiáng)度和前一個(gè)時(shí)間段的網(wǎng)絡(luò)安全態(tài)勢(shì)建立不同的預(yù)測(cè)模型,實(shí)驗(yàn)結(jié)果表明利用前二個(gè)時(shí)間段的攻擊強(qiáng)度建立的模型預(yù)測(cè)效果較好,將該模型命名為AP-Kalman算法。AP-Kalman算法預(yù)測(cè)精度比GRE-Kalman算法高,說明AP-Kalman算法是可行的。
[Abstract]:With the increasing scale of the network and the increasingly complex and changeable structure, the traditional method to solve the single network security problem has been unable to meet the demand. The perception and prediction of the whole operation of the network has become one of the research hotspots in the field of network security. As an important part of network security situation awareness, network security situation prediction changes network security management from passive to active. The current network security situation prediction only uses the current and past network security situation values to predict the future for a period of time. This method has a single prediction data and does not combine with various environmental factors. In view of the above problems, the main work and innovation of this paper are as follows: (1) the environmental factors that affect the network security situation are deeply studied. Because there are many environmental factors affecting network security situation, in order to weigh the accuracy and efficiency of prediction, the grey correlation entropy analysis method is used to select the influential factors which have a large degree of correlation with network security situation. A complete network security situational awareness model based on grey association entropy and Kalman is presented. (2) GRE-Kalman prediction algorithm is proposed. Combined with the selected key factors of network security situation, a Kalman prediction algorithm (GRE-Kalman) based on grey association entropy is proposed. The GRE-Kalman prediction model is suitable for any influence factor, and the number of influencing factors can be determined according to the need. The prediction accuracy and the adaptability of the algorithm are improved by combining the influence factors. (3) the AP-Kalman prediction algorithm is proposed. Combined with the key factors selected by the grey association entropy analysis method, the attack intensity of the previous time period, the first two time periods, the first three time periods, the attack intensity of the first three time periods, the attack intensity of the previous time period, the attack intensity of the first three time periods, respectively. Different prediction models are established between the attack intensity of the previous time period and the network security situation of the previous time period. The experimental results show that the prediction effect of the model based on the attack intensity of the previous two time periods is good. The model is named AP-Kalman algorithm. The prediction accuracy of AP-Kalman algorithm is higher than that of GRE-Kalman algorithm, which shows that AP-Kalman algorithm is feasible.
【學(xué)位授予單位】：南京航空航天大學(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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本文編號(hào)：2356723