Abstract
Webshell is a backdoor web page-based program. Malicious attackers obtain some privileges through the Webshell so as to realize the operation and control of the website. However, due to confusion coding technology, Webshell detection becomes difficult. This paper presents a Webshell detection model based on the word attention mechanism. In the model, we mainly focus on intra-line word association. After using Word2vec to vectorize the words, we use GRU (Gated Recursive Unit) and the attention mechanism to train and detect the samples. The experimental results show that the model has a high detection rate and low loss function.
Highlights
Webshell is a web page program written in ASP, PHP, JSP, CGI, or other web scripting languages
Recurrent Neural Network (RNN) [16] has good performance in many NLP tasks; this paper considers solving the problem of Webshell detection using RNN
This paper presents a Webshell detection model based on the attention mechanism [20]
Summary
Webshell is a web page program written in ASP, PHP, JSP, CGI, or other web scripting languages. Reference [10] used text features of opcode sequences and common statistical features of PHP files to propose a PHP Webshell detection method based on the RFGBDT (Random Forest Gradient Boosting Decision Tree). T. Li et al.: Webshell Detection Based on the Word Attention Mechanism model, which combines the Random Forest [11], [12] and Gradient Boosting Decision Tree [13] algorithms. Li et al.: Webshell Detection Based on the Word Attention Mechanism model, which combines the Random Forest [11], [12] and Gradient Boosting Decision Tree [13] algorithms These methods have high detection rates, they are all aimed at PHP-type Webshells.
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