【摘要】：隨著信息時(shí)代的到來(lái),互聯(lián)網(wǎng)得以快速發(fā)展和普及。但是與此同時(shí),也給人們的生活帶來(lái)了嚴(yán)重的危害,那便是網(wǎng)絡(luò)安全事件頻繁發(fā)生。盡管現(xiàn)在有各種各樣的網(wǎng)絡(luò)安全設(shè)備用來(lái)保護(hù)互聯(lián)網(wǎng)安全,但是由于他們是針對(duì)不同的安全問(wèn)題而設(shè)計(jì)的,具有專一性和不同的側(cè)重點(diǎn),所以這就導(dǎo)致他們不能很好地評(píng)估和預(yù)測(cè)整個(gè)網(wǎng)絡(luò)的安全狀態(tài)。在本論文中,通過(guò)對(duì)當(dāng)前網(wǎng)絡(luò)安全態(tài)勢(shì)評(píng)估和預(yù)測(cè)方法的詳細(xì)分析和研究,表明當(dāng)前如何提高網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)的預(yù)測(cè)精度和提高預(yù)測(cè)的收斂速度仍然是需要解決的熱點(diǎn)問(wèn)題。本文針對(duì)提高網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)的預(yù)測(cè)精度問(wèn)題,提出了基于二分K-均值的徑向基神經(jīng)網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)模型,該方法使用二分K-均值聚類算法確定徑向基神經(jīng)網(wǎng)絡(luò)的數(shù)據(jù)中心和擴(kuò)展函數(shù),彌補(bǔ)了徑向基神經(jīng)網(wǎng)絡(luò)數(shù)據(jù)中心難以確定的缺陷。經(jīng)實(shí)驗(yàn)驗(yàn)證,該方法在一定條件下提高了預(yù)測(cè)精度。本文針對(duì)提高網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)的收斂速度問(wèn)題,提出了基于改進(jìn)的人工免疫的網(wǎng)絡(luò)安全態(tài)勢(shì)預(yù)測(cè)方法,該方法使用了等價(jià)劃分區(qū)間的策略來(lái)改進(jìn)人工免疫算法,彌補(bǔ)了在隨機(jī)生成初始抗體的過(guò)程中產(chǎn)生的數(shù)據(jù)冗余性缺陷。經(jīng)實(shí)驗(yàn)驗(yàn)證,該方法提高了預(yù)測(cè)的收斂速度。
[Abstract]:With the arrival of the information age, the Internet has been rapidly developed and popularized. But at the same time, it also brings serious harm to people's life, that is, network security incidents occur frequently. Although there are a variety of network security devices available to protect Internet security, because they are designed for different security issues, they are specific and have different emphases. So this leads to their inability to evaluate and predict the security of the entire network. In this paper, through the detailed analysis and research on the current network security situation assessment and prediction methods, it is shown that how to improve the prediction accuracy and convergence rate of network security situation prediction is still a hot issue to be solved. In order to improve the prediction accuracy of network security situation prediction, a radial basis function neural network security situation prediction model based on dichotomous K-means is proposed in this paper. In this method, the binary K-means clustering algorithm is used to determine the data center and the expansion function of the radial basis function neural network, which makes up for the difficulty of determining the data center of the radial basis function neural network. The experimental results show that this method can improve the prediction accuracy under certain conditions. In order to improve the convergence rate of network security situation prediction, a network security situation prediction method based on improved artificial immune is proposed in this paper. It makes up for the data redundancy in the process of generating the initial antibody randomly. Experimental results show that the proposed method improves the convergence rate of prediction.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.08

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