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

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