The rapid development of the Internet has changed the way people live and work. Web security, as the foundation of network security, has received much more attention. Based on the variability of Webshells and the vulnerability of detection methods, this paper proposed a model that used deep learning to detect and implements the automatic identification of Webshells. For the shortcomings of the traditional detection models using machine learning algorithms, this paper proposed to apply convolutional neural network to Webshell detection process. The deep learning model does not require complicated artificial feature engineering, and the modeled features trained through model learning can also allow the attacker to avoid targeted bypassing in Webshell detection. The experimental results showed that this method not only has better detection accuracy, but also can effectively avoid the attacker's targeted bypassing. At the same time, with the accumulation of training samples, the detection accuracies of the detection model in different application environments will gradually improvements, which has clear advantages over traditional machine learning algorithms.

In the version of this chapter that was originally published, the acknowledgment of the NSFC Foundation was missing. The chapter has been updated to include these corrections.