三思而后行：利用原始URL和HTML特征检测钓鱼网页
Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics
AI总结：
本文提出WebPhish，一种利用原始URL和HTML内容嵌入及卷积神经网络进行端到端训练的钓鱼网页检测方法，实验准确率达98.1%，优于基线方法。
摘要：
钓鱼网站传播未经请求的内容，并经常被用于实施电子邮件和互联网欺诈；在任何用户信息被提交之前检测出它们至关重要。近年来，人们已经做出了多种努力来检测这些钓鱼网站。大多数现有方法使用从网站文本内容中手工制作的词汇和统计特征来训练分类模型以检测钓鱼网页。然而，这些钓鱼检测方法面临一些挑战，包括：1）提取手工制作特征的繁琐性，这需要专门的领域知识来确定哪些特征对特定平台有用；2）基于手工制作特征构建的模型难以捕捉URL和HTML内容中单词和字符的语义模式。为了解决这些挑战，本文提出了WebPhish，一种端到端的深度神经网络，使用嵌入的原始URL和HTML内容进行训练，以检测网站钓鱼攻击。首先，所提出的模型自动采用嵌入技术将相应的字符提取为同质的稠密向量。然后，拼接层合并URL和HTML嵌入矩阵。接着，使用卷积层来建模其语义依赖关系。使用真实世界的钓鱼数据进行了大量实验，取得了98.1%的准确率，表明WebPhish在识别钓鱼页面方面优于基线检测方法。
英文摘要：
Phishing websites distribute unsolicited content and are frequently used to commit email and internet fraud; detecting them before any user information is submitted is critical. Several efforts have been made to detect these phishing websites in recent years. Most existing approaches use hand-crafted lexical and statistical features from a website's textual content to train classification models to detect phishing web pages. However, these phishing detection approaches have a few challenges, including 1) the tediousness of extracting hand-crafted features, which require specialized domain knowledge to determine which features are useful for a particular platform; and 2) the difficulties encountered by models built on hand-crafted features to capture the semantic patterns in words and characters in URL and HTML content. To address these challenges, this paper proposes WebPhish, an end-to-end deep neural network trained using embedded raw URLs and HTML content to detect website phishing attacks. First, the proposed model automatically employs an embedding technique to extract the corresponding characters into homologous dense vectors. Then, the concatenation layer merges the URL and HTML embedding matrices. Following that, Convolutional layers are used to model its semantic dependencies. Extensive experiments were conducted with real-world phishing data, which yielded an accuracy of 98.1\%, showing that WebPhish outperforms baseline detection approaches in identifying phishing pages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
