CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites

By Guang Xiang, Jason I. Hong, Carolyn Penstein Rosé, Lorrie Faith Cranor. 2011

570 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

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Citations in the corpus

DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “…URL and its domain name [16, 14, 17, 18], from its HTML content and structure, and from other sources, including search engines, HTTP cookies, website certiﬁ-cates [19, 20, 10, 21, 22, 23, 24, 25], and even publicly-available blacklisting services like Google Safe Browsing and PhishTank [26].”
An Anti-Phishing System Employing Diffused Information (2014, methodology): “Currently, two major approaches can be identi.ed: (1) blacklist-based techniques (e.g. Google Safe Browsing [Staff 2012a]) block phishing emails or Web pages by comparing URLs against a remote database of known phishing sites; (2) heuristic techniques (e.g. Spoofguard [Chou et al. 2004], CANTINA”
Malicious URL Detection using Machine Learning: A Survey (2017, methodology): “[51], [52] proposed CANTINA and its variants for detecting phishing websites using a comprehensive featurebased machine learning approach, by exploiting various fea-”
Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, background): “They further developed 8 discriminatory features and proposed CANTINA+ which explored HTML Document Object Model (DOM) and third party services to find phishing pages [39].”
Predicting phishing websites based on self-structuring neural network (2013, methodology): “The effectiveness of the heuristic-based methods, sometimes called features-based methods, depends on picking a set of discriminative features that could help in distinguishing the type of website [8].”
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection (2018, background): “Researchers have proposed several types of features for this task, including blacklist features [12, 26], lexical features [20, 26, 27, 41], host-based features [5, 26, 30], content features [3, 37, 40], and context and popularity based features [4, 7, 24].”