On the Effectiveness of Techniques to Detect Phishing Sites

By Christian Ludl, Sean McAllister, Engin Kirda, Christopher Krügel. 2007

208 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].”
PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “For instance, PhishTank only contains 40% of all phishing URLs, but 100% of all PayPal phish.”
Learning to Detect Phishing Webpages (2014, background): “…is asked to reveal private information such as password, bank account information, credit card number, social security number, etc. Whittaker et al. [42] define a phishing webpage as ”any webpage that, without permission, alleges to act on behalf of a third party with the intention of confusing…”
An Empirical Analysis of Phishing Blacklists (2009, methodology): “In Nov 2006, Ludl et. al used 10,000 phishing URLs from Phishtank to test the effectiveness of the blacklists maintained by Google and Microsoft [21].”
Social Phishing (2018, methodology): “Ludl et al. use 10,000 phishing URLs to test the effectiveness of the blacklists maintained by Google and Microsoft [17].”
Large-Scale Automatic Classification of Phishing Pages (2010, methodology): “Despite the noise in the training data, our classifier learns a robust model for identifying phishing pages which correctly classifies more than 90% of phishing pages several weeks after training concludes.”
Behind Phishing: An Examination of Phisher Modi Operandi (2008, background): “Ludl et al. [14] examine the effectiveness of multiple classes of detection methods.”
Attribute Inference Attacks in Online Social Networks (2018, background): “Moreover, we assume the users and the service providers are not taking other steps (e.g., obfuscating social friends [28] or behaviors [13, 53, 60]) to defend against inference attacks.”
Proactive Discovery of Phishing Related Domain Names (2012, background): “Google Safe Browsing or Microsoft Smart Screen are two examples and their efficiency has been proved in [14].”
A survey of the applications of text mining in financial domain (2016, background): “Various Studies of Phishing Detection Study Dataset Model Performance Measure Abdelhamid et al. (2014) Millersmiles/ PhishTank Associative Rule Accuracy Chen et al. (2011) Millersmiles DT/ NN/ SVM Accuracy He et al. (2011) Millersmiles\ PhishTank\ 3Sharp SVM True Positive/ False Positive Aburrous et”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, background): “Although phishing as an attack mechanism is well studied [1, 6, 16, 27, 13], little is known about its victims or effectiveness.”