發(fā)布時(shí)間：2018-07-10 13:46

  本文選題：災(zāi)變灰預(yù)測(cè) + 殘差改進(jìn)��； 參考：《河南大學(xué)》2014年碩士論文

【摘要】：針對(duì)電力行業(yè)內(nèi)傳統(tǒng)網(wǎng)絡(luò)安全預(yù)測(cè)無法全面反映系統(tǒng)整體狀況,預(yù)測(cè)精度不高的缺點(diǎn),提出了一種網(wǎng)絡(luò)安全預(yù)測(cè)方法。首先,對(duì)企業(yè)內(nèi)的網(wǎng)絡(luò)安全事件進(jìn)行分析,運(yùn)用層次分析法(AHP)構(gòu)建網(wǎng)絡(luò)安全的指標(biāo)體系,并對(duì)樣本數(shù)據(jù)進(jìn)行過濾與處理,考慮異常值的分布序列結(jié)構(gòu):然后使用災(zāi)變灰預(yù)測(cè)進(jìn)行模型的建模；最后對(duì)預(yù)測(cè)結(jié)果使用神經(jīng)網(wǎng)絡(luò)方法進(jìn)行改進(jìn),從而達(dá)到提高預(yù)測(cè)精度的目的。通過仿真實(shí)驗(yàn),表明基于殘差改進(jìn)的災(zāi)變灰預(yù)測(cè)方法的可行性和有效性。本文主要成果如下： 1對(duì)企業(yè)網(wǎng)絡(luò)安全相關(guān)數(shù)據(jù)進(jìn)行定期地采集,并進(jìn)行分析整理,使用主成分分析法(PCA)提取出包含信息量較大的因素構(gòu)成評(píng)價(jià)指標(biāo),將此作為企業(yè)網(wǎng)絡(luò)安全預(yù)測(cè)的基礎(chǔ)； 2對(duì)過濾后的信息進(jìn)行處理,運(yùn)用層次分析法(AHP)得出指標(biāo)權(quán)重,構(gòu)建網(wǎng)絡(luò)安全評(píng)價(jià)的指標(biāo)體系,為日后企業(yè)網(wǎng)絡(luò)安全工作的開展提供依據(jù)； 3在樣本數(shù)據(jù)分析的基礎(chǔ)上,通過對(duì)神經(jīng)網(wǎng)絡(luò)方法及灰色模型方法的綜合比較應(yīng)用,提出一種適用于行業(yè)現(xiàn)狀的預(yù)測(cè)模型,并進(jìn)行數(shù)據(jù)處理,作為預(yù)測(cè)模型的輸入,采用災(zāi)變灰預(yù)測(cè)模型對(duì)時(shí)間序列進(jìn)行預(yù)測(cè),并將結(jié)果代入神經(jīng)網(wǎng)絡(luò)模型進(jìn)行修正,得到準(zhǔn)確的網(wǎng)絡(luò)安全預(yù)測(cè)值。
[Abstract]:Aiming at the shortcoming that the traditional network security prediction in the electric power industry can not reflect the whole system situation and the prediction accuracy is not high, a network security prediction method is proposed. First of all, the network security events in enterprises are analyzed, the index system of network security is constructed by AHP, and the sample data is filtered and processed. Considering the distribution sequence structure of outliers, the model is modeled with catastrophe grey prediction, and the prediction result is improved by neural network method to improve the prediction accuracy. The simulation results show the feasibility and effectiveness of the disaster grey prediction method based on residual error. The main achievements of this paper are as follows: 1 collecting and analyzing the related data of enterprise network security regularly, using principal component analysis (PCA) to extract the factors which contain a large amount of information to constitute the evaluation index. This is regarded as the basis of enterprise network security prediction. 2 the filtered information is processed and the index weight is obtained by using AHP to construct the index system of network security evaluation. It provides the basis for the work of enterprise network security in the future. 3 on the basis of sample data analysis, the comprehensive comparison between neural network method and grey model method is carried out. This paper presents a forecasting model suitable for the present situation of the industry, and carries on the data processing. As the input of the prediction model, the catastrophic grey prediction model is used to predict the time series, and the result is modified by the neural network model. Get accurate network security prediction value.
【學(xué)位授予單位】：河南大學(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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