基于卷积神经网络的异常流量鉴别方法

Identification Method of Abnormal Traffic based on Convolution Neural Network

入侵检测系统是网络安全的重要组成部分。针对已知网络攻击的检测,深度学习和传统机器学习都存在查准率和准确率低,以及对重要特征难以有效提取的问题,提出一种基于卷积神经网络结构的异常流量鉴别方法CNN-BDF。对入侵数据建立神经网络,在卷积网络后引入批归一化层,并使用Flatten函数作用于卷积层到全连接层的过渡,最后在全连接层中间引入Dropout层。采用NSL-KDD数据集进行模型评估,实验结果表明,CNN-BDF模型的准确率和查准率分别达到89.01%和84.72%,较基于传统机器学习与深度学习的入侵检测模型具有更好的效果。

Intrusion detection system is an important part of network security.For the detection of known network attacks,both deep learning and traditional machine learning have low precision and accuracy,and it is difficult to effectively extract important features.Aiming at these problems,an abnormal traffic identification method CNN-BDF based on convolutional neural network structure is proposed in this paper.The neural network is established for the intrusion data,the batch normalization layer is introduced after the convolutional network,and the Flatten function is used to act on the transition from the convolutional layer to the fully connected layer.Finally,the Dropout layer is introduced in the middle of the fully connected layer.The NSL-KDD data set is used to evaluate the model.The experimental results show that the accuracy and precision of the CNN-BDF model reach 89.01%and 84.72%respectively,which shows better performance than the intrusion detection model based on traditional machine learning and deep learning.