本文關(guān)鍵詞： 云平臺(tái) 主動(dòng)防護(hù)技術(shù) 數(shù)據(jù)優(yōu)化 異常檢測(cè) 異�；謴�(fù)　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),云平臺(tái)技術(shù)的廣泛應(yīng)用和快速發(fā)展,并采用專業(yè)的服務(wù)器集中管理,普遍具有良好的可用性和安全性。網(wǎng)絡(luò)環(huán)境中的惡意攻擊,云平臺(tái)自身的復(fù)雜性以及大規(guī)模性使得云平臺(tái)系統(tǒng)經(jīng)常發(fā)生異常,引起部分甚至所有服務(wù)失效。因此,云平臺(tái)安全主動(dòng)防護(hù)技術(shù)已成為領(lǐng)域的研究熱點(diǎn)。本文面向云平臺(tái)安全,從網(wǎng)絡(luò)安全和主機(jī)安全兩個(gè)層次開(kāi)展主動(dòng)防護(hù)技術(shù)研究,針對(duì)云平臺(tái)安全異常,進(jìn)行實(shí)時(shí)異常分析、系統(tǒng)檢測(cè)和系統(tǒng)恢復(fù),重在增強(qiáng)云平臺(tái)可用性。本文的工作主要有以下三點(diǎn)。首先,為了獲取云平臺(tái)網(wǎng)絡(luò)和主機(jī)運(yùn)行信息,設(shè)計(jì)云平臺(tái)安全主動(dòng)防護(hù)異常監(jiān)控系統(tǒng),對(duì)云平臺(tái)網(wǎng)絡(luò)攻擊信息和服務(wù)器運(yùn)行數(shù)據(jù)進(jìn)行概率統(tǒng)計(jì)分析,使用基于概率統(tǒng)計(jì)的特征優(yōu)化算法和基于數(shù)據(jù)類型的特征優(yōu)化算法對(duì)數(shù)據(jù)集進(jìn)行優(yōu)化。針對(duì)云平臺(tái)網(wǎng)絡(luò)和主機(jī)等異常檢測(cè)問(wèn)題,提出基于信息密度貝葉斯算法的異常檢測(cè)方法,構(gòu)造數(shù)據(jù)特征概率集合,通過(guò)引入信息熵來(lái)表示信息的不確定度,定義信息密度以描述信息不確定度分布狀態(tài),對(duì)算法進(jìn)行了詳細(xì)描述,并分析了其時(shí)間復(fù)雜度。通過(guò)實(shí)驗(yàn)對(duì)攻擊數(shù)據(jù)進(jìn)行分析,表明方法可有效減少數(shù)據(jù)信息損失,提高檢測(cè)準(zhǔn)確率,并縮短檢測(cè)時(shí)間。其次,在分析異常恢復(fù)技術(shù)及其優(yōu)缺點(diǎn)的基礎(chǔ)上,設(shè)計(jì)了一種基于狀態(tài)度量值的異�；謴�(fù)方法,計(jì)算出集群的狀態(tài)度量值,通過(guò)設(shè)置備份恢復(fù)節(jié)點(diǎn),記錄異常前系統(tǒng)正確的運(yùn)行狀態(tài)。根據(jù)異常恢復(fù)的策略,系統(tǒng)進(jìn)行檢查點(diǎn)設(shè)置,提供安全狀態(tài)恢復(fù)機(jī)制和恢復(fù)協(xié)議,并保持狀態(tài)節(jié)點(diǎn)記錄一致性,將云平臺(tái)系統(tǒng)正確運(yùn)行狀態(tài)存儲(chǔ)到系統(tǒng)備份存儲(chǔ)器。實(shí)驗(yàn)表明,當(dāng)云平臺(tái)運(yùn)行發(fā)生異常,可有效進(jìn)行安全狀態(tài)恢復(fù)。最后,設(shè)計(jì)并實(shí)現(xiàn)了云平臺(tái)安全防護(hù)系統(tǒng),搭建實(shí)驗(yàn)平臺(tái)并收集各項(xiàng)性能數(shù)據(jù),對(duì)系統(tǒng)各模塊的性能進(jìn)行驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,該系統(tǒng)可以有效減少數(shù)據(jù)處理時(shí)間的損耗,降低了信息的損失,可以有效較低誤檢率,提高檢測(cè)準(zhǔn)確率,具備良好的檢測(cè)和安全恢復(fù)性能。
[Abstract]:In recent years, with the wide application and rapid development of cloud platform technology, and the use of professional server centralized management, generally have good availability and security. Malicious attacks in the network environment, The complexity and large scale of cloud platform make cloud platform system often abnormal, causing some or even all service failure. Therefore, cloud platform security active protection technology has become a research hotspot in the field of cloud platform security. From the network security and host security two levels of active protection technology research, aimed at cloud platform security anomalies, real-time anomaly analysis, system detection and system recovery, The main work of this paper is to enhance the usability of cloud platform. Firstly, in order to obtain cloud platform network and host operation information, the cloud platform security active protection anomaly monitoring system is designed. On the cloud platform network attack information and server running data for the probability and statistical analysis, The feature optimization algorithm based on probability and statistics and the feature optimization algorithm based on data type are used to optimize the data set. An anomaly detection method based on information density Bayesian algorithm is proposed to solve the anomaly detection problems such as cloud platform network and host computer. The information entropy is introduced to represent the uncertainty of information, the information density is defined to describe the distribution of information uncertainty, and the algorithm is described in detail. The analysis of the attack data shows that the method can effectively reduce the loss of data information, improve the detection accuracy, and shorten the detection time. Based on the analysis of abnormal recovery technology and its advantages and disadvantages, an anomaly recovery method based on state measure is designed, and the state measure of cluster is calculated, and the backup recovery node is set up. According to the strategy of abnormal recovery, the system sets up checkpoint, provides security state recovery mechanism and recovery protocol, and keeps the record consistency of state node. The cloud platform system running state is stored in the system backup memory. The experiment shows that when the cloud platform operation is abnormal, the security state can be recovered effectively. Finally, the cloud platform security protection system is designed and implemented. The experimental results show that the system can effectively reduce the loss of data processing time, reduce the loss of information, and effectively reduce the false detection rate. Improve detection accuracy, with good detection and safety recovery performance.
【學(xué)位授予單位】：北京交通大學(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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