A Webshell Detection Model Based On Bayes

Abstract

Webshell is a command-page document that exists in the form of execution of the program, also known as a backdoor, is an essential means of hacking website uses. Due to the great harm of webshell, webshell detection has become a key research direction in network security. At present, there are many types of research and results in webshell detection, such as the commonly used detection tools such as “safe dog”, “Dshield”, and “Hippo”. Most of these tools are based on the signature to detect, and the detection accuracy is not ideal. By analyzing the current status of Webshell detection methods, this paper proposes a Webshell detection method based on the Naive Bayes algorithm, and uses Python for implementation and results analysis. This model can effectively detect Webshell that has been fuzzy coded, and improve the accuracy of detection.