集美大学学报（自然科学版）2025，Vol.30Issue(2)：179-185,7.DOI:10.19715/j.jmuzr.2025.02.10
基于Stacking集成学习的恶意URL识别方法
Malicious URL Recognition Method Based on Stacking Ensemble Learning
摘要
Abstract
In allusion to the problems of traditional URL detection methods such as low accuracy and poor real-time performance in detecting malicious URLs,an algorithm model based on Stacking ensemble learning is proposed,which uses five machine learning models:ADB,LR,SVM,GBDT and GNB as primary classifiers.Its pluralistic structure enables different machine learning models to complement each other and improve detection Overall system performance.The performance evaluation is performed on the test set in turn,and the best per-formance is selected.The experimental results indicate that on many metrics,such as recall,accuracy,preci-sion,F1 value,the overall performance of integrated learning models is better than the traditional machine learning models,the accuracy of malicious URL detection can reach 96.77％.关键词
恶意URL/机器识别/Stacking模型/集成学习/基学习器Key words
malicious URL/machine recognition/Stcking model/integrated learning/base learner分类
计算机与自动化引用本文复制引用
孙杨,邱祥锋..基于Stacking集成学习的恶意URL识别方法[J].集美大学学报（自然科学版）,2025,30(2):179-185,7.基金项目
福建省自然科学基金项目"大规模图数据的自适应分布式存储与查询技术研究"(2022J01336) （2022J01336）
