URL Phishing Detection using Machine Learning Techniques based on URLs Lexical Analysis

Mohammed AbuTaha, M. Ababneh, Khaled W. Mahmoud, Sherenaz W. Al-Haj Baddar
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引用次数: 11

Abstract

Phishing URLs mainly target individuals and/or organizations through social engineering attacks by exploiting the humans’ weaknesses in information security awareness. These URLs lure online users to access fake websites, and harvest their confidential information, such as debit/credit card numbers and other sensitive information. In this work, we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. The experiments were carried out on a dataset that originally contained 1056937 labeled URLs (phishing and legitimate). This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the analyzed URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL.
基于URL词法分析的机器学习技术的URL钓鱼检测
网络钓鱼url主要利用人类在信息安全意识方面的弱点,通过社会工程攻击来针对个人和/或组织。这些网址引诱在线用户访问虚假网站,并获取他们的机密信息,如借记卡/信用卡号码和其他敏感信息。在这项工作中,我们介绍了一种基于URL词汇分析和机器学习分类器的网络钓鱼检测技术。实验是在最初包含1056937个标记url(网络钓鱼和合法)的数据集上进行的。该数据集被处理生成22个不同的特征,这些特征使用不同的特征约简技术进一步减少到更小的集合。对随机森林分类器、梯度增强分类器、神经网络分类器和支持向量机分类器进行了评价,结果显示支持向量机分类器的优势,对被分析url的检测准确率最高,达到99.89%。我们的方法可以整合到互联网浏览器的附加组件/中间件功能中,以便在在线用户试图仅使用其URL访问钓鱼网站时发出警告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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