基于RFE+SVM的卷积神经网络在入侵检测方面的应用

Application of Convolutional Neural Network based on RFE+SVM in Intrusion Detection

神经网络在入侵检测方向的使用已经是入侵检测领域的热门发展方向。传统入侵检测方法如机器学习、数据挖掘、统计分析等都具有一定局限性。通过引入基于RFE+SVM降维的卷积神经网络算法,从Python的深度学习库(tensorflow)出发,搭建出一类基于卷积神经网络的入侵检测数据分类模型。通过数据集对比及实验证明,该模型有效且稳定的提高了对异常数据的判别率,并可发现未知的攻击类型。

The use of neural network in intrusion detection has been a hot development direction in the field of intrusion detection.Traditional intrusion detection methods such as machine learning,data mining,statistical analysis have some limitations.By introducing convolutional neural network algorithm based on RFE+SVM dimension reduction and starting from Python’s tensorFlow,a data classification model of Intrusion Detection Based on convolutional neural network is built.Through the comparison of data sets and experiments,it is proved that the model can effectively and stably improve the discrimination rate of abnormal data,and can find unknown attack types.