發(fā)布時(shí)間：2018-01-29 14:59

  本文關(guān)鍵詞： 網(wǎng)絡(luò)安全 態(tài)勢(shì)感知 遺傳算法 神經(jīng)網(wǎng)絡(luò) 態(tài)勢(shì)評(píng)估　出處：《寧夏大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：計(jì)算機(jī)技術(shù)和通信技術(shù)高速發(fā)展,網(wǎng)絡(luò)攻防技術(shù)不斷革新,網(wǎng)絡(luò)威脅加劇,信息系統(tǒng)安全受到嚴(yán)重挑戰(zhàn)。新常態(tài)下的互聯(lián)網(wǎng)環(huán)境下的網(wǎng)絡(luò)防御手段和攻擊手段的發(fā)展出現(xiàn)了嚴(yán)重的不平衡,智能化安全防御體系的研究成為熱點(diǎn)。數(shù)據(jù)融合技術(shù)的發(fā)展使得網(wǎng)絡(luò)安全評(píng)估向綜合性發(fā)展,但這些技術(shù)局限于反映過(guò)去和當(dāng)前的網(wǎng)絡(luò)態(tài)勢(shì)狀況,難以適應(yīng)如今復(fù)雜多變的網(wǎng)絡(luò)環(huán)境,網(wǎng)絡(luò)安全的發(fā)展傾向于被動(dòng)防御轉(zhuǎn)為主動(dòng)防御,智能化、可視化、全局化等網(wǎng)絡(luò)安全的特點(diǎn)顯得尤為突出,機(jī)器學(xué)習(xí)、大數(shù)據(jù)挖掘等方法和技術(shù)的研究和運(yùn)用加速了網(wǎng)絡(luò)防御技術(shù)的發(fā)展,特別是可視化前端技術(shù)的發(fā)展為網(wǎng)絡(luò)安全態(tài)勢(shì)感知體系的動(dòng)態(tài)展示提供了良好的技術(shù)支撐。本文通過(guò)研究區(qū)域網(wǎng)絡(luò)信息系統(tǒng)中安全事件,結(jié)合網(wǎng)絡(luò)中硬件采集的日志信息,運(yùn)用智能化的態(tài)勢(shì)分析的方法使網(wǎng)絡(luò)管理者通過(guò)直觀的可視化展示判斷當(dāng)前網(wǎng)絡(luò)安全狀況,感知未來(lái)網(wǎng)絡(luò)安全狀況并及時(shí)做出調(diào)整,保護(hù)網(wǎng)絡(luò)資產(chǎn)不受損失,提高信息系統(tǒng)網(wǎng)絡(luò)攻擊防范能力�；�于GA-RBF神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)安全態(tài)勢(shì)感知系統(tǒng)的研究和實(shí)現(xiàn)主要工作包括:1、網(wǎng)絡(luò)安全態(tài)勢(shì)評(píng)估方法和關(guān)鍵技術(shù)的研究。分析和對(duì)比不同的態(tài)勢(shì)評(píng)估方法,參考已有的網(wǎng)絡(luò)安全態(tài)勢(shì)指標(biāo)處理成果,對(duì)區(qū)域網(wǎng)絡(luò)中的各種網(wǎng)絡(luò)安全因素包括(硬件資產(chǎn)、設(shè)備狀況、安全警報(bào)、事件日志等)進(jìn)行清洗、采集和維度劃分,采用模糊層次分析法劃分當(dāng)前范圍內(nèi)的網(wǎng)絡(luò)安全態(tài)勢(shì)值,從而建立全面完善的網(wǎng)絡(luò)安全態(tài)勢(shì)指標(biāo)體系。經(jīng)過(guò)分析區(qū)域網(wǎng)絡(luò)中的拓?fù)浣Y(jié)構(gòu)和組織結(jié)構(gòu)并參考國(guó)內(nèi)外學(xué)者已有的信息系統(tǒng)風(fēng)險(xiǎn)評(píng)估成果,利用數(shù)據(jù)融合技術(shù)構(gòu)建評(píng)估指標(biāo)體系應(yīng)該遵循從點(diǎn)到面、自內(nèi)而外的評(píng)估策略。通過(guò)態(tài)勢(shì)理解技術(shù)的分析,對(duì)防火墻、服務(wù)器等硬件設(shè)施的日志記錄和掃描結(jié)果等經(jīng)過(guò)權(quán)重處理和一致化檢驗(yàn)的信息進(jìn)行關(guān)聯(lián)分析,建立威脅傳播網(wǎng)絡(luò)數(shù)據(jù)集。2、基于GA-RBF神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)模型的構(gòu)建。本文比較了幾種常見的態(tài)勢(shì)預(yù)測(cè)方法,確定了基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的態(tài)勢(shì)預(yù)測(cè)方法,結(jié)合已有的實(shí)驗(yàn)環(huán)境和數(shù)據(jù)構(gòu)建出適合用于區(qū)域網(wǎng)絡(luò)信息系統(tǒng)安全的網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)模型。模型利用遺傳算法從模擬種族進(jìn)化的過(guò)程中找出適合的控制因子集合和參數(shù)因子集合的組合方式,對(duì)集合進(jìn)行自然選擇,交叉、變異,能很好地克服反饋緩慢問(wèn)題,通過(guò)定義非線性時(shí)間和數(shù)據(jù)集合的關(guān)系,來(lái)構(gòu)建預(yù)測(cè)模型應(yīng)用到系統(tǒng)中。3、安全態(tài)勢(shì)感知系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)。根據(jù)已有的網(wǎng)絡(luò)安全態(tài)勢(shì)評(píng)估模型與預(yù)測(cè)模型相結(jié)合,從傳統(tǒng)的系統(tǒng)設(shè)計(jì)入手,對(duì)網(wǎng)絡(luò)安全態(tài)勢(shì)感知系統(tǒng)進(jìn)行需求分析和功能定位,搭建基于區(qū)域網(wǎng)絡(luò)信息系統(tǒng)安全的網(wǎng)絡(luò)安全態(tài)勢(shì)感知系統(tǒng)的總體架構(gòu)。通過(guò)對(duì)感知系統(tǒng)進(jìn)行總體設(shè)計(jì),確定態(tài)勢(shì)采集層、態(tài)勢(shì)理解分析層、態(tài)勢(shì)預(yù)測(cè)層、態(tài)勢(shì)感知層、態(tài)勢(shì)展示層等子系統(tǒng)的功能和交互流程,基于各子系統(tǒng)的特點(diǎn)來(lái)分析支撐平臺(tái)和架構(gòu)設(shè)計(jì)中的實(shí)現(xiàn)方法,通過(guò)分析各模塊之間的數(shù)據(jù)流向,為各個(gè)子系統(tǒng)提出方案解決并實(shí)現(xiàn)具體的系統(tǒng)。
[Abstract]:The rapid development of computer technology and communication technology, network attack and defense technology innovation, network information system security threats, serious challenges. The development of network defense and attack means the norm under the Internet environment have resulted in a serious imbalance, the research of intelligent security defense system has become a hot spot. The development of data fusion technology makes network security evaluation to the comprehensive development, but the technology is limited to reflect the past and current network status, now it is difficult to adapt to the complex network environment, the development of network security tend to passive defense to active defense, intelligence, visualization, characteristics of global network security is particularly prominent, machine learning. The research and application of data mining method and technology has accelerated the development of network defense technology, especially the development of front-end technology for network visualization It provides a good technical support to the dynamic display of network security situation awareness system. Through the research of information system security incidents in the regional network, combined with the log information acquisition hardware in the network analysis method, the intelligent use situation of the network manager through intuitive visual display to judge the current situation of network security, perceived future network security the situation and make timely adjustments to protect network assets, improve the ability to prevent network attacks. The information system research and implementation of network security situation awareness system GA-RBF neural network the main work includes: 1, based on the research of the network security situation evaluation method and key technology. The evaluation method of analysis and comparison of different situation, with reference to the existing the network security situation index processing results, including a variety of network security factors in regional network (hardware assets, equipment condition, safety Alarm and event log etc.) for cleaning, collection and dimensions, using fuzzy AHP method to divide the current range of network security situation, so as to establish a system of comprehensive network security situation index. Through the risk analysis of information system topology and organization structure in regional network structure and the reference of domestic and foreign scholars have been the evaluation results the use of technology, constructing evaluation index system should follow from the point of data fusion, from the inside out assessment strategies. Through analysis of situation understanding technology, firewall, server hardware facilities such as log records and scanning results after processing and consistency of test weight information for association analysis, the establishment of threat propagation network data set.2 prediction model is constructed based on GA-RBF neural network. This paper compares several common forecasting methods of the situation, determined based on radial basis function neural The prediction method of network situation, combined with the experimental data and the environment has been constructed for the network security situation prediction model of regional network information system security model. By using the genetic algorithm from simulation evolved find control factor sets and parameters suitable for the combination for the subset of the set of natural selection, crossover, mutation that can be a good way to overcome the slow feedback problem, through the relationship between the definition of nonlinear time and data collection, to construct the prediction model is applied to the system of.3, the design and implementation of security situation awareness system. According to the model and prediction model of network security situation assessment has been combined, starting from the traditional system design, requirements analysis and the function of network security situation awareness system, build the overall framework of network security situation of regional network information system security awareness system based on The overall design of the structure. Through the sensing system to determine the situation situation understanding collection layer, analysis layer, situation prediction layer, situation awareness layer, situation display function and interactive process layer subsystems, the characteristics of each subsystem is analyzed based on the realization method of the supporting platform and architecture design, through the analysis of the data flow between the various modules for each subsystem, and proposes solutions to the specific implementation.

【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.08;TP183

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