Research on Detection Methods of Phishing Websites Based on Improved Random Forest Algorithm

ZHU Qi; LIN Guo-yuan

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Research on Detection Methods of Phishing Websites Based on Improved Random Forest Algorithm

更新时间：2026-09-04

- Research on Detection Methods of Phishing Websites Based on Improved Random Forest Algorithm
- Vol. 36, Issue 4, Pages: 43-46(2019)
- 作者机构：
  
  1. 中国矿业大学计算机科学与技术学院,江苏,徐州,221116
  2. 矿山数字化教育部工程研究中心机构,江苏,徐州,221116
  3. 南京大学计算机软件新技术国家重点实验室机构,江苏,南京,210049
- 作者简介：
- 基金信息：
- DOI：
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- Published：2019
- 稿件说明：
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朱琪, 林果园. 基于改进随机森林算法的钓鱼网站检测方法研究[J]. 微电子学与计算机, 2019, 36(4): 43-46, 51.

ZHU Qi, LIN Guo-yuan. Research on Detection Methods of Phishing Websites Based on Improved Random Forest Algorithm[J]. 2019, 36(4): 43-46.
朱琪, 林果园. 基于改进随机森林算法的钓鱼网站检测方法研究[J]. 微电子学与计算机, 2019, 36(4): 43-46, 51. DOI：

ZHU Qi, LIN Guo-yuan. Research on Detection Methods of Phishing Websites Based on Improved Random Forest Algorithm[J]. 2019, 36(4): 43-46. DOI：

摘要

为了更准确快捷的对钓鱼网站进行识别

提出了一种基于改进随机森林算法的钓鱼网站检测方法.该方法挖掘钓鱼网页特征之间潜在的关联规则

并对数据集进行分区

以此区分特征数据的重要程度并计算权重以及数据选取的比例

选取数据后对数据空间进行相应的集合化与剪辑以此优化森林的建立

并根据建立的森林达到对钓鱼网站检测识别的目的.最终实验说明

该方法对钓鱼网站的检测识别具有很好的效果和效率.

Abstract

In order to improve the efficiency of phishing detection

a new algorithm was proposed to improve the traditional random forest algorithm. Potential association rules between web features are mined and used to partition the data set

in order to distinguish the features of different structures and calculate the weight of different data space to determine the scale of the selection. After selection of data

training data sets need to be aggregated and clipped to optimize the establishment of forests. Websites are trained and predicted using voting in decision forest. Experiments result shows that the new algorithm has obvious advantages in efficiency and effectiveness compared with the other two algorithm.

关键词

Keywords

references

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AMARATUNGA D, CABRERA J, LEE Y. Enriched random forests[J]. Bioinformatics, 2008, 24(18) : 2010-2014. DOI: 2010

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ZHANG W W, GONG J, LIU Q, et al.Lightweight domain name detection algorithm based on morpheme features[J]. Ruan Jian XueBao/Journal of Software, 2016, 27(9):2348-2364. DOI: 2348

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王燕, 王兴芬, 任俊玲.面向钓鱼网站敏感特征项选取的IIGAIN算法[J].计算机应用与软件, 2016, 33(4):297-301. DOI: 297

张茜, 延志伟, 李洪涛, 等.网络钓鱼欺诈检测技术研究[J].网络与信息安全学报, 2017, 3(7):7-24. DOI: 7

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