Financial phishing detection method based on sensitive characteristics of webpage

Xiang-dong HU; Ke LIU; Feng ZHANG; Jia-fu LIN; Jun FU; Zhi-hui GUO

doi:10.11959/j.issn.2096-109x.2017.00122

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Financial phishing detection method based on sensitive characteristics of webpage

Academic paper | 更新时间：2024-06-03

- Financial phishing detection method based on sensitive characteristics of webpage
- Chinese Journal of Network and Information Security Vol. 3, Issue 2, Pages: 31-38(2017)
- 作者机构：
  
  1. 重庆邮电大学自动化学院，重庆 400065
  2. 中国移动通信有限公司研究院，北京 100033
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2017.00122
  CLC： TP393.08
- Online First：2017-02，
  
  Published：15 February 2017
- 稿件说明：
移动端阅览
Xiang-dong HU, Ke LIU, Feng ZHANG, et al. Financial phishing detection method based on sensitive characteristics of webpage[J]. Chinese Journal of Network and Information Security, 2017, 3(2): 31-38.
DOI：

Xiang-dong HU, Ke LIU, Feng ZHANG, et al. Financial phishing detection method based on sensitive characteristics of webpage[J]. Chinese Journal of Network and Information Security, 2017, 3(2): 31-38. DOI： 10.11959/j.issn.2096-109x.2017.00122.

摘要

提出一种基于页面敏感特征的金融类钓鱼网页检测方法，通过获取网页超文本标记语言特定标签中的文本信息，利用适合中文的多模式匹配算法（AC_SC

AC suitable for Chinese）匹配出敏感文本条数，计算出敏感文本特征值；定位截取网页的logo图像，采用PCA-SIFT算法提取图像特征，并与预先建立的网页logo图像库进行匹配，计算出logo图像相似度；基于文本特征值和图像相似度实现对金融类钓鱼网页的判定。实验结果表明，该方法具有很强的针对性和时效性，并能取得不低于97%的召回率。

Abstract

A financial phishing detection method based on sensitive characteristics of webpage was proposed

which acquired sensitive text information of specific hypertext markup language tags and computes sensitive text eigen-value. The method matches number of sensitive text using multiple pattern matching algorithm AC_SC (AC suitable for Chinese). Then

the method locates and cuts logo image of webpage

and utilizes PCA-SIFT algorithm to extract image features and match features with library of webpage logo which was established beforehand. Meanwhile

it calculates similarity of two logo image. Finally

the decision can be concluded by the text eigenvalue and image similarity. It shows that the method is better in pertinence and timeliness according to experiment

and achieves no less than 97% detection accuracy.

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Keywords

references

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