Multi-source information comprehensive malicious domain name detection based on convolutional neural network

As the communication carrier of malware, viruses and malicious servers, malicious domain names pose a threat to social public information security. Aiming at the problem that the characteristics of malicious domain names change all the time, which leads to a low accuracy of traditional malicious domain name detection models, this paper proposes a multi-source information comprehensive malicious domain name detection model based on convolutional neural network. Firstly, different types of features in Domain Name System are divided into five categories, and a convolutional neural network framework is designed for each category to reduce the mutual influence between different types of features. Secondly, perform feature extraction is carried out and corresponding weights are trained, and multiple classification results are fused by decision pool to capture information among various features. Finally, the experimental results show that our scheme has higher prediction accuracy than other machine learning algorithms.