Abstract: Intrusion detection is an important link in the field of information security protection. With the development of network technology, the active defense against network intrusions becomes more and more important, and intrusion data becomes more massive, complex, and unbalanced, which leads to the detection performance of the traditional intrusion detection technology is relatively low, so how to improve the detection performance of the intrusion detection system for unbalanced data sets is a huge challenge. The traditional CNN model has a good performance for processing complex data, but its effect of dealing with imbalanced data set is not very good. In order to solve this problem, an intrusion detection method based on Focal Loss and convolutional neural network is proposed. Different from traditional convolutional neural network, this model uses the Focal Loss function to solve the data imbalance problem, and in the convolutional layer, a regularization method (DropBlock) is added to improve the generalization ability of the model. Experiments of using KDD 99 data set show that the accuracy and precision of intrusion detection of this model are improved compared with the traditional intrusion detection model.

Key words: intrusion detection, network security, convolutional neural network, regularization, imbalanced data set, Focal Loss