Application research of Webshell detection system based on deep learning

Aiming at the limitations of traditional Webshell detection methods, such as relying on static rule matching, easy to be affected by confusing technologies, and difficult to cope with constantly evolving attack means. A Webshell detection method based on deep learning is proposed. This method not only integrates convolutional neural network (CNN) algorithm and recurrent neural network (RNN) algorithm, but also introduces multi-dimensional strategies of opcode analysis, regular detection and entropy detection to enrich the detection means. It is proved that this algorithm greatly improves the accuracy and real-time of Webshell detection.