信息网络安全 ›› 2017, Vol. 17 ›› Issue (9): 45-48.doi: 10.3969/j.issn.1671-1122.2017.09.011

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一种基于机器学习的网页分类技术

孙靖超()   

  1. 中国人民公安大学信息技术与网络安全学院,北京 100038
  • 收稿日期:2017-08-01 出版日期:2017-09-20 发布日期:2020-05-12
  • 作者简介:

    作者简介: 孙靖超(1993—),男,山东,硕士研究生,主要研究方向为网络攻防。

A Classification Method of Web Page Using Machine Learning

Jingchao SUN()   

  1. College of Information Technology and Network Security, People’s Public Security University of China, Beijing 100038, China
  • Received:2017-08-01 Online:2017-09-20 Published:2020-05-12

摘要:

随着网络的普及,网页的数量飞速增长,混杂其中的恶意网页占据的比例也呈上升趋势。恶意网页的检测一直是网络安全领域的研究重点和难点,传统的恶意网页检测模型在新形势下的表现不尽如人意。机器学习算法在恶意网页领域的应用是突破传统恶意网页检测局限的一种途径。文章开发出一个基于机器学习的对恶意网页检测的模型,该模型通过收集诸如URL、主机信息和各种网页内容的特征信息并通过机器学习算法对网页进行分类,与前人工作相比达到了更好的分类效果。

关键词: 恶意网页, 钓鱼网页, 机器学习, 前端安全

Abstract:

With the popularity of the network, the number of web pages is in rapid growth, and the proportion of the malicious web page growth rate also shows an upward trend. The detection of malicious web pages has been the focus and difficulty of network security research. The traditional malicious webpage detection model is more and more difficult to deal with the new situation. The application of machine learning algorithm in the field of malicious webpage is a way to break the limitation of traditional malicious webpage. In this paper, a malicious web page detection model based on machine learning has been developed. The model classifies the web pages by collecting the characteristic information like URL、HOST information and the content of various web pages using machine learning algorithm, and it has achieved more excellent classification effect comparing to the prior work.

Key words: malicious Web page, phishing webpage, machine learning, front end security

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