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Phishing websites distribute unsolicited content and are frequently used tocommit email and internet fraud; detecting them before any user information issubmitted is critical. Several efforts have been made to detect these phishingwebsites in recent years. Most existing approaches use hand-crafted lexical andstatistical features from a website's textual content to train classificationmodels to detect phishing web pages. However, these phishing detectionapproaches have a few challenges, including 1) the tediousness of extractinghand-crafted features, which require specialized domain knowledge to determinewhich features are useful for a particular platform; and 2) the difficultiesencountered by models built on hand-crafted features to capture the semanticpatterns in words and characters in URL and HTML content. To address thesechallenges, this paper proposes WebPhish, an end-to-end deep neural networktrained using embedded raw URLs and HTML content to detect website phishingattacks. First, the proposed model automatically employs an embedding techniqueto extract the corresponding characters into homologous dense vectors. Then,the concatenation layer merges the URL and HTML embedding matrices. Followingthat, Convolutional layers are used to model its semantic dependencies.Extensive experiments were conducted with real-world phishing data, whichyielded an accuracy of 98.1\%, showing that WebPhish outperforms baselinedetection approaches in identifying phishing pages.

Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics