本文選題：G-K算法 + 網(wǎng)絡(luò)安全　； 參考：《科技通報(bào)》2017年11期

【摘要】：針對普通Kalman算法在網(wǎng)絡(luò)安全態(tài)勢預(yù)測中對初始數(shù)據(jù)的依賴性較高,且預(yù)測精度不夠高的問題,本文提出了一種基于G-K算法的網(wǎng)絡(luò)安全態(tài)勢預(yù)測模型。首先利用灰關(guān)聯(lián)熵分析方法選出影響網(wǎng)絡(luò)安全態(tài)勢的關(guān)鍵因素,然后結(jié)合關(guān)鍵因素建立網(wǎng)絡(luò)安全態(tài)勢的多元關(guān)系模型,最后選用KDD-cup99的部分?jǐn)?shù)據(jù)作為實(shí)驗(yàn)數(shù)據(jù)源對改進(jìn)算法進(jìn)行實(shí)例仿真。結(jié)果表明,G-K算法能夠快速跟蹤網(wǎng)絡(luò)安全態(tài)勢的變化趨勢,預(yù)測精度優(yōu)于普通Kalman算法。
[Abstract]:In order to solve the problem that the common Kalman algorithm has high dependence on the initial data in the network security situation prediction and the prediction accuracy is not high enough, this paper proposes a network security situation prediction model based on the G-K algorithm. First, the key factors that affect the network security situation are selected by the grey relational entropy analysis method, and then the key factors are combined with the key factors. The multi relation model of network security situation is established. Finally, some data of KDD-cup99 are selected as the experimental data source to simulate the improved algorithm. The results show that the G-K algorithm can quickly track the trend of network security situation, and the prediction accuracy is better than the common Kalman algorithm.
【作者單位】： 山西電力職業(yè)技術(shù)學(xué)院;青島科技大學(xué)自動(dòng)化學(xué)院;
【分類號(hào)】：TP393.08
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本文編號(hào)：2100423