基于模糊SVM模型的计算机网络入侵识别

Computer Network Intrusion Recognition Based on Fuzzy SVM Model

大数据量环境下为提高计算机网络入侵识别精准度,实现对更多的数据类别的识别,提出了一种新的网络入侵识别算法,利用该算法从中挖掘入侵行为数据。该算法在DBSCAN算法的基础上,引入SVM模型,开发出SVM-DBSCAN双训练器应用下的网络入侵识别模型,并配备运行程序。实验测试结果显示,该算法分类准确率大于96.78%,可以作为网络入侵识别工具。

In order to improve the accuracy of computer network intrusion identification,from the perspective of increasing the amount of data,more data categories are identified,and a new network intrusion identification algorithm is proposed,which is used to mine intrusion behavior data.Based on the DBSCAN algorithm,the algorithm introduces the SVM model,develops the network intrusion identification model under the application of svm-dbscan dual trainer,and is equipped with running program.The experimental results show that the classification accuracy of the modified algorithm is higher than 96.78%,which can be used as a network intrusion recognition tool.