Behind Phishing: An Examination of Phisher Modi Operandi

By D. Kevin McGrath, Minaxi Gupta. 2008

287 citations recorded by the source corpus.

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Citations in the corpus

PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “[27] analyze the modus operandi of phishers, the characteristics of phishing URLs, the domains, and their hosting infrastructure.”
Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, background): “performed a comparative analysis of phishing and nonphishing URLs [30].”
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].”
Identifying suspicious URLs: an application of large-scale online learning (2009, background): “We are motivated by prior studies noting distinguishing commonalities in such features for malicious URLs (Chou et al., 2004; McGrath & Gupta, 2008).”
There Is No Free Phish: An Analysis of "Free" and Live Phishing Kits (2008, background): “A number of studies have focused on the operational aspects of phishing, for example, the impact of take-down actions [17], the infrastructure used for hosting phishing pages [15], and the effectiveness of manual assessing of phishing reports [18].”
PhishNet: Predictive Blacklisting to Detect Phishing Attacks (2010, methodology): “Our work derives important observations about basic differences between phishing and non-phishing URLs from [4].”
Malicious URL Detection using Machine Learning: A Survey (2017, methodology): “[11] studied the impact of a few host-based features on the maliciousness of URLs.”
Detecting algorithmically generated malicious domain names (2010, methodology): “[22] D. K. McGrath and M. Gupta.”