Webshell detection scheme based on BERT and XGBoost
Webshell,the backdoor program of Web services,is a common means of hacker attack.The traditional detection meth-ods have the defects of high missed detection rate and false positive rate when detecting the Webshell backdoor which is mutated and confused-encrypted.To solve this problem,this paper integrated BERT and XGBoost features to design a new detection method,which could greatly improve the detection accuracy of Webshell backdoor program.In the detection,the word vector fea-tures were extracted from the preprocessed Webshell sample files using BERT model,and the integrated learning algorithm XG-Boost was used for classification training,so as to obtain an optimal detection model.Finally,the model could effectively detect various Webshell malicious programs.Compared with the detection model based on traditional machine learning algorithm,the proposed fusion Webshell detection method had better performance in the aspects of precision,recall and F1 value,and the de-tection accuracy reached 97.75%.
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