本文選題：神經(jīng)網(wǎng)絡(luò) + 入侵檢測(cè)　； 參考：《計(jì)算機(jī)仿真》2013年09期

【摘要】：研究網(wǎng)絡(luò)節(jié)點(diǎn)危險(xiǎn)程度評(píng)估優(yōu)化入侵檢測(cè)問(wèn)題。由于入侵的多樣性和隨機(jī)性,造成準(zhǔn)確檢測(cè)困難。傳統(tǒng)的網(wǎng)絡(luò)安全模型都是對(duì)信譽(yù)度或信任度等概念完成惡意節(jié)點(diǎn)整體檢測(cè),因?yàn)閱蝹�(gè)節(jié)點(diǎn)屬性較為復(fù)雜,所承擔(dān)的作用不同,使得針對(duì)單個(gè)節(jié)點(diǎn)信息評(píng)估過(guò)程較為粗糙,很難設(shè)定準(zhǔn)確閥值進(jìn)行精確判斷,造成傳統(tǒng)模型對(duì)單個(gè)節(jié)點(diǎn)危險(xiǎn)程度評(píng)估不準(zhǔn)。提出一種危險(xiǎn)程度的網(wǎng)絡(luò)節(jié)點(diǎn)惡意程度評(píng)估模型,使用馬爾科夫算法與貝葉斯學(xué)習(xí)器計(jì)算單個(gè)節(jié)點(diǎn)的危險(xiǎn)度,運(yùn)用貝葉斯方法推斷出節(jié)點(diǎn)惡意程度的解空間,依據(jù)節(jié)點(diǎn)的屬性特征計(jì)算節(jié)點(diǎn)的惡意度,克服傳統(tǒng)方法不能對(duì)單個(gè)節(jié)點(diǎn)做出判斷的弊端。實(shí)驗(yàn)表明,與已有的安全模型相比,提出的安全管理模型對(duì)惡意節(jié)點(diǎn)具有更高的檢測(cè)率。
[Abstract]:Research on network node risk assessment and optimization of intrusion detection problem. Due to the diversity and randomness of intrusion, it causes accurate detection difficulties. The evaluation process of the node information is relatively rough, it is difficult to set accurate thresholds for accurate judgment, which causes the traditional model to evaluate the risk degree of single node. A risk degree evaluation model of network node malware is proposed, and the Markoff algorithm and Bayesian classifier are used to calculate the risk degree of a single node, and the Bayesian formula is used. The method deduce the solution space of the node's malicious degree, calculate the malicious degree of the node according to the attribute characteristics of the node, overcome the disadvantage that the traditional method can not judge the single node. The experiment shows that the proposed security management model has a higher detection rate to the malicious node compared with the existing security model.
【作者單位】： 貴州大學(xué) 計(jì)算機(jī)科學(xué)與信息學(xué)院;
【分類號(hào)】：TP393.08

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本文編號(hào)：1902418