Malicious URL detection system based on LSTM and attention mechanism

Abstract

With the rapid development of the Internet, network security has gradually received everyone's attention and attention. In network security, Web application security is particularly important. Therefore, for the identification and detection of malicious URLs, this paper constructs a new feature extraction method based on traditional machine learning algorithms, and proposes a deep learning model based on the LSTM algorithm and adding the Attention mechanism. The results show that the accuracy rate of the model reaches 98.9%, and it can complete the malicious URL detection task well. On this basis, a malicious URL detection system based on the LSTM+Attention mechanism was developed to improve the security of cyberspace.