Research on Malicious URL Identification and Analysis for Network Security

With the rapid development of the Internet, the emergence of various malicious URLs seriously endangers the national network information security and user information security. Therefore, it is of great theoretical significance and practical value for network security to accurately identify and deal with malicious URLs. This paper proposes a research method of character level feature extraction and recognition of malicious URLs based on CNN + BiLSTM + CNN model. Based on the massive URL data sets, the parameter distribution characteristics of malicious URLs are analyzed, and the skip gram model is introduced to unsupervised train the preprocessed data sets, so as to embed the characters of URLs. Then the CNN + BiLSTM + CNN model is introduced to extract and optimize the local and temporal features of malicious URLs. The experimental results show that on the same data set, the malicious URL recognition method based on CNN + BiLSTM + CNN model has better recognition effect and higher accuracy than the traditional BiLSTM based algorithm and CNN based algorithm. The F1 value is increased to 98.14%, and the average iteration time is greatly reduced. It shows that the research method proposed in this paper has good applicability in the field of malicious URL identification for network security.