Abstract
With the rapid development of hacker technology, network security issues have become increasingly serious. Uploading WebShell is one of the most common attack methods used by network intruders. WebShell escape technology is changing with each passing day, and the traditional method based on feature matching is difficult to accurately detect. In order to detect WebShell more accurately and mitigate the threat caused by WebShell attacks, a WebShell detection method combining bidirectional GRU (gated recurrent unit) and attention mechanism is proposed for the first time. First, the sample is preprocessed to remove useless information such as annotations. Then, the sample is divided into a series of words, the word2vec model is used to obtain the word vector, and finally, the word vector is input into the network for prediction. According to the experimental results, compared with peer methods, the method in this study performs better in performance indicators such as accuracy rate, recall rate, and F1 value. The model not only detects the PHP-type WebShell but also has a good performance on the WebShell written in JSP, ASPX, or ASP languages. The detection accuracy of PHP-type, JSP-type, and ASP-type WebShell reached 99.36%, 99.23%, and 99.87%, respectively, and the recall rate was 98.6%, 99.13%, and 99.56%, respectively.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call