Bidirectional IndRNN malicious webpages detection algorithm based on convolutional neural network and attention mechanism

Article type: Research Article

Authors: Wang, Huan-Huan^a | Tian, Sheng-Wei^{a; b; *} | Yu, Long^c | Wang, Xian-Xian^a | Qi, Qing-Shan^a | Chen, Ji-Hong^a

Affiliations: [a] School of Software, Xinjiang University, Urumqi, Xinjiang, P.R. China | [b] School of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, P.R. China | [c] Network Center, Xinjiang University, Urumqi, Xinjiang, P.R. China

Correspondence: [*] Corresponding author. Sheng-wei Tian, China, E-mail: [email protected].

Abstract: A convolutional neural network combined with attention mechanism and a parallel joint algorithm model (CATTB) of bidirectional independent recurrent neural network are proposed. The algorithm extracts the relocation feature and the “texture fingerprint” feature for expressing the similarity of the URL (Uniform Resource Locator) binary file content of the malicious web page, and uses the word vector tool word2vec to train the URL word vector feature and extract the URL static vocabulary feature. CNN (Convolutional Neural Network) is used to extract deep local features. Secondly, Attention mechanism adjusts weight and BiIndRNN (Bidirectional Independently Recurrent Neural Network) to extract global features. Finally, softmax is used for classification. This paper extracts more comprehensive features from different angles and using different methods. The experimental results show that the test results are higher than other researchers, and compared with other algorithms, the proposed CATTB algorithm improves the accuracy of malicious web page detection.

Keywords: Malicious webpages, convolutional neural network, attention mechanism, bidirectional independently recurrent neural network

DOI: 10.3233/JIFS-190455

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1929-1941, 2020