网络钓鱼邮件防护:基于文本和URL分析的网络钓鱼邮件检测技术

J. Hajgude, L. Ragha
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引用次数: 23

摘要

网络钓鱼是社会工程和技术攻击的结合,旨在说服受害者提供个人信息,通常是为了攻击者的金钱收益。网络钓鱼邮件包含引诱受害者执行某些操作的信息,例如点击托管网络钓鱼网站的URL,或执行恶意软件代码。网络钓鱼已经成为网络犯罪中最流行的做法。网络钓鱼攻击变得越来越频繁和复杂。电子邮件的URL和文本内容分析将导致一个高度准确的反网络钓鱼电子邮件分类器。我们提出了一种综合考虑黑名单、白名单和启发式技术的优点来提高准确率和降低误报率的技术。在启发式技术中,我们使用电子邮件的文本分析和URL分析。由于大多数网络钓鱼邮件内容相似,我们提出的方法通过分析邮件的文本内容和词法URL分析来提高性能。如果黑名单中存在实际链接中的DNS,则检测钓鱼邮件。如果DNS出现在白名单中,则认为它是合法的DNS。如果不在黑名单和白名单中,则通过与现有网络钓鱼DNS、邮件中发现的内容和分析实际URL进行模式匹配来分析。在黑名单和白名单的帮助下,我们避免了网络钓鱼和合法电子邮件的检测时间。同时结合DNS、邮件文本内容分析、URL分析等特点,降低误报率。
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“Phish mail guard: Phishing mail detection technique by using textual and URL analysis”
Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker. Phishing emails contains messages to lure victims into performing certain actions, such as clicking on a URL where a phishing website is hosted, or executing a malware code. Phishing has become the most popular practice among the criminals of the Web. Phishing attacks are becoming more frequent and sophisticated. URL and textual content analysis of email will results in a highly accurate anti phishing email classifier. We propose a technique where we consider the advantages of blacklist, white list and heuristic technique for increasing accuracy and reducing false positive rate. In heuristic technique we are using textual analysis and URL analysis of e-mail. Since most of the phishing mails have similar contents, our proposed method will increase the performance by analysing textual contents of mail and lexical URL analysis. It will detect phishing mail if DNS in actual link is present in blacklist. DNS is present in white list then it is considered as legitimate DNS. If it is not present in blacklist as well as white list then it is analyzed by using pattern matching with existing phishing DNS, contents found in mail and analysis of actual URL. With the help blacklist and white list we are avoiding detection time for phishing and legitimate email. At the same time we are decreasing false positive rate by combining features of DNS, textual content analysis of email and URL analysis.
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