基于優(yōu)化數(shù)據(jù)處理的深度信念網(wǎng)絡模型的入侵檢測方法
發(fā)布時間：2018-10-19 06:40
【摘要】：針對目前網(wǎng)絡中存在的對已知攻擊類型的入侵檢測具有較高的檢測率,但對新出現(xiàn)的攻擊類型難以識別的缺陷問題,提出了一種基于優(yōu)化數(shù)據(jù)處理的深度信念網(wǎng)絡(DBN)模型的入侵檢測方法。該方法在不破壞已學習過的知識和不嚴重影響檢測實時性的基礎上,分別對數(shù)據(jù)處理和方法模型進行改進,以解決上述問題。首先,將經(jīng)過概率質量函數(shù)(PMF)編碼和MaxMin歸一化處理的數(shù)據(jù)應用于DBN模型中;然后,通過固定其他參數(shù)不變而變化一種參數(shù)和交叉驗證的方式選擇相對最優(yōu)的DBN結構對未知攻擊類型進行檢測;最后,在NSL-KDD數(shù)據(jù)集上進行了驗證。實驗結果表明,數(shù)據(jù)的優(yōu)化處理能夠使DBN模型提高分類精度,基于DBN的入侵檢測方法具有良好的自適應性,對未知樣本具有較高的識別能力。在檢測實時性上,所提方法與支持向量機(SVM)算法和反向傳播(BP)網(wǎng)絡算法相當。
[Abstract]:At present, the intrusion detection of known attack types in the network has a high detection rate, but it is difficult to identify the new attack types. An intrusion detection method based on (DBN) model based on optimized data processing is proposed. In order to solve the above problems, the method improves the data processing and the method model separately on the basis of not destroying the knowledge that has been learned and not seriously affecting the real-time detection. First, the data encoded by the probabilistic quality function (PMF) and normalized by MaxMin are applied to the DBN model. The unknown attack type is detected by fixing other parameters and changing one parameter and cross-validating method by selecting the relatively optimal DBN structure. Finally, the method is verified on the NSL-KDD data set. The experimental results show that the optimal processing of data can improve the classification accuracy of DBN model. The intrusion detection method based on DBN has good adaptability and high recognition ability to unknown samples. In real time detection, the proposed method is comparable to the support vector machine (SVM) (SVM) algorithm and the backpropagation (BP) network algorithm.
【作者單位】： 遼寧工程技術大學軟件學院;
【基金】：遼寧省教育廳科學技術研究項目(LJYL052)~~
【分類號】：TP393.08
[Abstract]:At present, the intrusion detection of known attack types in the network has a high detection rate, but it is difficult to identify the new attack types. An intrusion detection method based on (DBN) model based on optimized data processing is proposed. In order to solve the above problems, the method improves the data processing and the method model separately on the basis of not destroying the knowledge that has been learned and not seriously affecting the real-time detection. First, the data encoded by the probabilistic quality function (PMF) and normalized by MaxMin are applied to the DBN model. The unknown attack type is detected by fixing other parameters and changing one parameter and cross-validating method by selecting the relatively optimal DBN structure. Finally, the method is verified on the NSL-KDD data set. The experimental results show that the optimal processing of data can improve the classification accuracy of DBN model. The intrusion detection method based on DBN has good adaptability and high recognition ability to unknown samples. In real time detection, the proposed method is comparable to the support vector machine (SVM) (SVM) algorithm and the backpropagation (BP) network algorithm.
【作者單位】： 遼寧工程技術大學軟件學院;
【基金】：遼寧省教育廳科學技術研究項目(LJYL052)~~
【分類號】：TP393.08
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