使用机器学习的在线网络钓鱼检测

Rabab Alayham Abbas Helmi, M. Johar, Muhammad Alif Sazwan Bin Mohd. Hafiz
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引用次数: 1

摘要

网络钓鱼检测越来越重要,随着对网络空间的依赖程度越来越高,所提出的系统提供了一个简单的解决方案,当用户不确定所访问网站的真实性时,他们可以尝试复制统一资源定位符(URL)并将链接粘贴到网络钓鱼检测系统中。通过系统流程,它将帮助用户识别给定的链接是合法网站还是钓鱼网站。因此,用户不会整天都在怀疑自己在某个网站上提供的信息是否安全。该系统将复杂的决策简化,可以帮助用户准确地检测给定URL的每个变量。机器学习不仅能够检测每个变量,而且系统还将学习确定URL内部的网络钓鱼元素。本文采用随机森林分类器和支持向量机(SVM)两种分类器来检测钓鱼元素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Phishing Detection Using Machine Learning
Online phishing detection has gained significant importance, which will only grow with the amount of dependency on cyberspace, the proposed system provides an easy solution, during cases when the user is unsure about the authenticity of the website visited, they can try to copy the Uniform Resource Locator (URL) and paste the link into the online phishing detection system. Through the system process, it will help the user to identify whether given links were legitimate website or it is a phishing website. Therefore, the user will not be in a doubtful situation the whole day in wondering whether the information they gave in a certain website is safe or not. Providing complex decision with simplicity, the system will help the user to detect each variable of URL given accurately. Machine learning will not only able to detect each of the variables, but the system will also learn to determine the element of phishing inside of the URL. Two classifiers are used in order to detect the element of phishing which are Random Forest classifiers and Support Vector Machine (SVM).
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