广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (3): 132-140.doi: 10.16088/j.issn.1001-6600.2021071201

• 研究论文 • 上一篇    下一篇

基于改进Stacking策略的钓鱼网站检测研究

胡强, 刘倩, 周杭霞*   

  1. 中国计量大学 信息工程学院, 浙江 杭州 310018
  • 收稿日期:2021-07-12 修回日期:2021-08-03 出版日期:2022-05-25 发布日期:2022-05-27
  • 通讯作者: 周杭霞(1963—), 女, 浙江杭州人, 中国计量大学教授。E-mail: zhx@cjlu.edu.cn
  • 基金资助:
    公安部重点实验室开放课题(2021DSJSYS004); 浙江省基础公益研究计划项目(LGF18F020017)

Study on Phishing Website Detection Based on Improved Stacking Strategy

HU Qiang, LIU Qian, ZHOU Hangxia*   

  1. College of Information Engineering, China Jiliang University, Hangzhou Zhejiang 310018, China
  • Received:2021-07-12 Revised:2021-08-03 Online:2022-05-25 Published:2022-05-27

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摘要： 针对目前大多数钓鱼网站检测技术准确率低、计算资源消耗大和检测不及时等问题,本文提出一种基于改进Stacking策略的钓鱼网站检测方法。该方法将多个分类表现优异的基学习器通过Stacking策略集成为一个高性能模型,并且把该Stacking算法第一级的输入特征与预测结果同时作为第二级的输入特征,充分发挥各模型精度高、速度快等优势,从而进一步提高模型性能。实验结果表明,与传统的机器学习钓鱼网站检测技术相比,在10万级数据集上,此集成学习算法在多个指标上都表现出更好的性能,精确率达到了97.82%,F1值达到97.54%,可以有效地检测钓鱼网站。

关键词: 钓鱼网站, 基学习器, Stacking算法, 特征提取, 集成学习

Abstract: Aiming at the problems of low accuracy of most detection technologies for phishing websites, high consumption of computing resources and untimely detection, a phishing website detection method based on an improved Stacking strategy is proposed. This method integrates multiple base learners with excellent classification performance into a high-performance model through stacking strategy, and takes the input characteristics and prediction results of the first level of the stacking algorithm as the input characteristics of the second level at the same time, so as to give full play to the advantages of high precision and fast speed of each model, and further improve the performance of the model. Experimental results show that, compared with traditional machine learning phishing website detection technology, this integrated learning algorithm on a 100,000-level data set shows better performance on multiple indicators, with accuracy rate of 97.82% and F1 value reach 97.54%, which can effectively detect phishing websites.

Key words: phishing website, base learner, Stacking algorithm, feature extraction, ensemble learning

中图分类号: 

  • TP393.08
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