Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network

摘要

This paper proposes a bidirectional LSTM algorithm (CBIR) based on convolutional neural network and independent recurrent neural network. The algorithm extracts the texture fingerprint feature used to express the similarity of the content of the URL binary file of the malicious webpages, and uses the word vector tool word2vec to train the URL word vector feature and extract the URL static vocabulary feature. The texture fingerprint feature, the URL word vector feature and the URL static vocabulary feature are merged, and the malicious webpages is analyzed and detected based on the CBIR algorithm model. Experimental results show that compared with other methods, the proposed CBIR algorithm has improved the accuracy of malicious webpages detection.