现代信息科技2024，Vol.8Issue(4)：171-174,179,5.DOI:10.19850/j.cnki.2096-4706.2024.04.035
基于SVM-DT-MLP模型的Web日志异常流量检测研究
Research on Web Log Abnormal Traffic Detection Based on the SVM-DT-MLP Model
摘要
Abstract
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.关键词
Web日志/异常流量检测/Stacking集成/Voting特征选择/机器学习Key words
Web log/abnormal traffic detection/Stacking integration/Voting feature selection/Machine Learning分类
信息技术与安全科学引用本文复制引用
魏璐露,程楠楠..基于SVM-DT-MLP模型的Web日志异常流量检测研究[J].现代信息科技,2024,8(4):171-174,179,5.基金项目
江西省教育厅科学技术研究项目(GJJ2202609) （GJJ2202609）
