Research on Web Log Abnormal Traffic Detection Based on the SVM-DT-MLP Model
With the popularity of Web applications,the risk of cyber attacks and security vulnerabilities is increasing.Web log files record the running information of websites in detail.Classifying the traffic in logs to detect abnormal attack traffic is one of the effective methods to ensure the long-term stability and security service provided by Web pages.In this paper,Voting feature selection and the Stacking integration are combined to construct the SVM-DT-MLP model,and it is used to detect abnormal traffic in Web logs.The test results show that the performance of SVM-DT-MLP model is significantly better than that of the single algorithm model,with the precision reaching 92.44％,the recall reaching 92.43％and the F1-Score reaching 92.44％.This means that the model can effectively detect abnormal attack traffic and has a good effect in ensuring stable and secure services provided by Web pages.
Web logabnormal traffic detectionStacking integrationVoting feature selectionMachine Learning
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