Abstract: Network intrusion detection is an important component of network security. At present, the popular detection technology is to use the traditional machine learning algorithm to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Depth learning is an algorithm that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

Key words: intrusion detection, deep learning, convolutional neural network, unsupervised, softmax

摘要： 网络入侵检测是网络安全的重要组成部分，目前比较流行的检测技术是使用传统机器学习算法对入侵样本进行训练从而获得入侵检测模型，但是这些算法具有检测率低的缺点。针对传统机器学习技术对于入侵检测准确率不高的情况，本文提出了一种基于卷积神经网络算法的网络入侵检测系统。该系统可以自动提取入侵样本的有效特征，从而对入侵样本进行准确分类。本系统在KDD99数据集上的检测准确率可到达96.83%，实验结果表明，基于卷积神经网络的入侵检测系统比传统机器学习技术具有更高的准确率。

关键词: 入侵检测, 深度学习, 卷积神经网络, 监督学习, 柔性最大值