URLBiDCNN: malicious URL detection based on bidirectional long short-term memory and deformable convolutional networks

Zhongyu Chen; Cong Ma; Shukai Duan

doi:10.1117/12.2674807

2 May 2023 URLBiDCNN: malicious URL detection based on bidirectional long short-term memory and deformable convolutional networks

Zhongyu Chen, Cong Ma, Shukai Duan

Author Affiliations +

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422X (2023) https://doi.org/10.1117/12.2674807
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

URLs (Uniform Resource Locators) are widely used on the Internet, but they are often used maliciously to carry out cyberattacks, causing significant losses to many enterprises and individuals. Therefore, it is crucial to spot those URLs to ensure network security. In this study, we propose a method for detecting malicious URLs that uses bidirectional LSTM and deformable convolutional network. First, the character vector corresponding to the URL is obtained by applying the embedding method, the bidirectional LSTM network is then fed the character vector to extract the global information in the URL, and the parallel deformable convolutional network receives the extracted data as input to learn multiple types of local area features. Finally, the fused local features are output to the FC network for URL classification. In this study, we conducted comparison experiments of different methods on different datasets. From the experimental results, on the three sampled datasets, the method's accuracy was 96.96%, 99.85%, and 96.43%, respectively, comparing with other research methods, the accuracy of the method proposed in this study for malicious URL detection was significantly improved.

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Zhongyu Chen, Cong Ma, and Shukai Duan "URLBiDCNN: malicious URL detection based on bidirectional long short-term memory and deformable convolutional networks", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422X (2 May 2023); https://doi.org/10.1117/12.2674807

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KEYWORDS

Deformation

Feature extraction

Convolution

Data modeling

Machine learning

Deep learning

Education and training

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