Learning to detect phishing emails

By Ian Fette, Norman M. Sadeh, Anthony Tomasic. 2007

768 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science

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Citations in the corpus

DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “Another line of work has considered the detection of phishing emails by analyzing their content along with that of the linked phishing webpages [27].”
PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “Our study presents accurate measurements of the duration and effectiveness of this popular threat, and discusses many new and interesting aspects we observed by monitoring hundreds of phishing campaigns.”
Beyond blacklists: learning to detect malicious web sites from suspicious URLs (2009, methodology): “use statistical methods in machine learning to classify phishing emails [8].”
Protecting people from phishing: the design and evaluation of an embedded training email system (2007, background): “Previous anti-phishing research has focused either on algorithms for detecting phishing attacks in web browsers [19], [39] or on evaluating the user interfaces of anti-phishing web browser toolbars [43].”
Behind Phishing: An Examination of Phisher Modi Operandi (2008, methodology): “(This fact has been used as a heuristic by work aiming to ﬁlter phishing e-mails [6].)”
The state of phishing attacks (2012, background): “Fette et al. 11 developed the first email phishing filter (in 2007) identifying several features that are highly indicative of phishing, including, for example, URLs that use different domain names.”
Machine learning for email spam filtering: review, approaches and open research problems (2019, methodology): “Akinyelu and Adewumi [90] proposed Random Forest for classifying phishing attacks with the method used by Fette et al [92] on a dataset comprising 2000 phishing and ham emails.”
An Anti-Phishing System Employing Diffused Information (2014, background): “1 Some wellknown email filtering solutions include Thunderbird [Staff 2008a] and PILFER [Fette et al. 2007].”
Predicting phishing websites based on self-structuring neural network (2013, background): “In [25], the authors compared a number of commonly used machine-learning methods including SVM, rule-based techniques, decision trees and Bayesian techniques.”
Phishing Attacks: A Recent Comprehensive Study and a New Anatomy (2021, methodology): “For email ﬁ ltering, ML techniques are commonly used for example in 2007, the ﬁ rst email phishing ﬁ lter was developed by authors in (Fette et al., 2007).”
Identifying suspicious URLs: an application of large-scale online learning (2009, background): “Fette et al. (2007) and Bergholz et al. (2008) examine select properties of URLs contained within an email to aid the machine learning classification of phishing emails (not the URLs themselves).”
Opinion spam and analysis (2008, background): “Another related research is email spam [8, 15, 22], which is also quite different from review spam.”
Phishing detection based Associative Classification data mining (2014, methodology): “In Sadeh et al. (2007), the authors compared some commonly used machine-learning methods including SVM, decision trees, and Naïve Bayes on the problem of email phishing.”
Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, methodology): “used machine learning to classify phishing messages [15].”
Learning to Detect Phishing Webpages (2014, methodology): “For clarity and to better understand the types of tactics used by phishers, URL-based features are further grouped into 4 broad categories and briefly describe them next.”