Malicious webpages analysis and detection algorithm based on BiLSTM. (21st October 2020)

• Record Type: Journal Article
• Authors: Wang, Huan-Huan; Yu, Long; Tian, Sheng-Wei; Luo, Shi-Qi; Pei, Xin-Jun
• Is Part Of: International journal of electronic business. Volume 15:Number 4(2019)
• Page Start: 351
• Page End: 367
• Publication Date: 2020-10-21
• Subjects: webpages -- bidirectional long short-term memory -- BiLSTM -- texture image -- deep learning; Electronic commerce -- Periodicals; 658.05467805
• Languages: English
• ISSNs: 1470-6067

Abstract

This paper proposes a bidirectional long short-term memory (BiLSTM) malicious webpages analysis and detection algorithm. Through the research on the characteristics of malicious webpages analysis and detection, the 'texture image' feature used to express the similarity of malicious webpages URL binary files is extracted; besides, the host information features and URL information features are extracted. The 'texture image' feature is integrated with host information features and URL information features, and a deep learning method of BiLSTM is used to analyse and detect malicious webpages. Compare to LSTM algorithm, k-nearest neighbourhood (KNN), IndRNN, CNN and Gaussian Bayes algorithm (Gaussian NB), the experimental results show that the algorithm has higher accuracy than the traditional model.