A Survey of URL-based Phishing Detection-猫眼课题宝

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2019

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Eint Sandi Aung;Chaw Thet Zan;H. Yamana

早稻田大学基础科学与工程研究生院计算机科学与通信工程系，日本东京，159-8555。电子邮件：a) eintsandiaung@toki.waseda.jp, b) chawthetzan@fuji.waseda.jp, c) yamana@waseda.jp摘要网络钓鱼被认为是一种盗窃个人信息的行为，其中网络钓鱼者（也称为攻击者）引诱用户交出敏感数据，如凭据、信用卡和银行账户信息、财务详细信息和其他行为数据。网络钓鱼检测正在成为一个重要的研究领域，随着网络钓鱼攻击数量的增加，越来越受到人们的关注。此外，由于攻击者正在创新各种技术，因此检测已成为开发人员的主要关注点。许多网络钓鱼检测方案已经内置到他们的架构中，如白名单，黑名单，内容，视觉相似性和基于url的一般。每种方法都有各自的优点和缺点。在这篇调查论文中，我们强调基于URL的网络钓鱼检测技术，因为我们认为URL是防止网络钓鱼攻击的重要标准。此外，检查基于url的特性还可以促进比其他方法更快的处理。在这项工作中，我们的目标是了解基于url的特征的结构，并研究它们的各种检测技术和机制。然后，我们根据不同数据集上URL特征的组合来分析性能。最后，我们总结了我们的发现，以促进更好的基于url的网络钓鱼检测系统。关键词网络钓鱼，基于url的，Web安全，特性

Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, 159-8555, Japan. E-mail : a) eintsandiaung@toki.waseda.jp, b) chawthetzan@fuji.waseda.jp, c) yamana@waseda.jp Abstract Cyber phishing is regarded as a theft of personal information in which phishers, also known as attackers, lure users to surrender sensitive data such as credentials, credit card and bank account information, financial details, and other behavioral data. Phishing detection is becoming a crucial research area, attracting increased focus as the number of phishing attacks grows. Furthermore, because attackers are innovating various techniques, detection has become a primary concern of developers. A number of phishing detection schemes has been built into their architecture, such as whitelist-, blacklist-, content, visual similarity and URL-based in general. Each has its individual advantages and drawbacks. In this survey paper, we emphasize on URL-based phishing detection techniques, because we consider the URL to be a significant criterium in preventing phishing attacks. Moreover, examining URL-based features can also encourage faster processing than other approaches. In this work, we aim to understand the structure of URL-based features and surveying their diverse detection techniques and mechanisms. We then analyze the performance based on the combinations of URL features on different datasets. Finally, we summarize our findings to promote better URL-based phishing detection systems. Keyword Phishing, URL-based, Web Security, Features

A Survey of URL-based Phishing Detection

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