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

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.