【摘要】：為了提高網絡入侵檢測正確率,利用特征選擇和支持向量機(SVM)參數(shù)間的相互聯(lián)系,提出一種特征選擇和SVM參數(shù)聯(lián)同步優(yōu)化的網絡入侵檢測算法.該算法首先將網絡入侵檢測正確率作為問題優(yōu)化的目標函數(shù),網絡特征和SVM參數(shù)作為約束條件建立數(shù)學模型,然后通過遺傳算法對數(shù)學模型進行求解,找到最優(yōu)特征子集和SVM參數(shù),最后利用KDD 1999數(shù)據集對算法性能進行測試.結果表明,相對于其他入侵檢測算法,同步優(yōu)化算法能夠較快選擇最優(yōu)特征與SVM參數(shù),有效提高了網絡入侵檢測正確率,加快了網絡入侵檢測速度.
[Abstract]:In order to improve the correct rate of network intrusion detection, a network intrusion detection algorithm based on feature selection and synchronous optimization of SVM parameters is proposed by using the relationship between feature selection and (SVM) parameters of support vector machines. The algorithm first takes the correct rate of network intrusion detection as the objective function of the problem optimization, the network features and SVM parameters as the constraints to establish a mathematical model, and then uses genetic algorithm to solve the mathematical model. The optimal feature subset and SVM parameters are found. Finally, the performance of the algorithm is tested by using the KDD 1999 dataset. The results show that compared with other intrusion detection algorithms, synchronous optimization algorithm can quickly select the optimal features and SVM parameters, effectively improve the correct rate of network intrusion detection, and accelerate the network intrusion detection speed.
【作者單位】： 平頂山學院軟件學院;平頂山學院計算機科學與技術學院;
【基金】：河南省科技計劃重點項目資助(102102210416)
【分類號】：TP393.08

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