网络钓鱼检测及特征选择在数据挖掘分类算法中的重要性综述

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引用次数: 0

摘要

在这个互联网时代,信息安全的问题达到了顶峰。网络世界的主要威胁之一是网络钓鱼攻击,这是一种电子邮件或网站欺诈方法,针对真正的网页或电子邮件,并在未经最终用户同意的情况下对其进行黑客攻击。有各种各样的技术可以帮助区分网站或电子邮件是合法的还是假的。在这些网络钓鱼欺诈的检测过程中,主要的贡献因素包括分类算法、特征选择技术或数据集准备方法以及特征提取,这些特征提取在检测和预防这些攻击中起着重要的作用。这篇调查报告研究了所有这些贡献者的影响,以及在最近的论文中进行的研究中应用的方法。实现的分类算法包括决策树、随机森林、支持向量机、逻辑回归、懒惰K星、朴素贝叶斯和J48等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms
: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.
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