Examining the impact of website take-down on phishing

By Tyler W. Moore, Richard Clayton. 2007

235 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

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.”
Measuring the Cost of Cybercrime (2012, background): “In one study, Moore and Clayton estimated that between 280,000 and 560,000 people gave away their credentials to phishing websites each year [44].”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, background): “Our findings suggest that in order to prevent a hijacked account’s exploitation, the reaction time to the credential compromise needs to be even faster than previously thought [18].”
So long, and no thanks for the externalities: the rational rejection of security advice by users (2009, background): “37% of users per year [28, 50] and each victim wastes 10 hours sorting it out, to be beneficial the daily effort of following the advice should be less than 0.”
The state of phishing attacks (2012, background): “27 When registering new domains, criminals look for names similar to the site they want to impersonate; for example, impersonating eBay, scam-mers might register ebay-login.com.”
A Hierarchical Adaptive Probabilistic Approach for Zero Hour Phish Detection (2010, methodology): “An analysis of rock-phishing sites by Moore et al [17] from February to April in 2007 reveals that 52.”
Temporal Correlations between Spam and Phishing Websites (2009, methodology): “Previous research has attempted to count victims, by using either a custom browser extension [1] or collecting server logs from phishing websites [5].”
PhishZoo: Detecting Phishing Websites by Looking at Them (2011, background): “But as most phishing sites are short-lived, last less than 20 hours [11], or change URLs frequently ( fast-flux ), the URL blacklisting approach fails to detect most phishing attacks.”
Traveling the silk road: a measurement analysis of a large anonymous online marketplace (2012, unknown): “, [13, 17, 18, 20, 22, 24, 25, 26, 27, 36]).”
An Empirical Analysis of Phishing Blacklists (2009, background): “The annual cost to consumers and businesses due to phishing in the US alone is estimated to be between $350 million and $2 billion [14, 26].”
Phishing Detection: A Literature Survey (2013, background): “• The phishing emails are communicated to the victims.”
The Plight of the Targeted Attacker in a World of Scale (2010, methodology): “Using the entirely independent method of counting cre-dentials at compromised phishing servers Moore and Clayton [45] estimate it at 0.34%.”
PhishNet: Predictive Blacklisting to Detect Phishing Attacks (2010, methodology): “For example, RockPhish gang [ 6 ] uses this method in their fraud infrastructure.”
Evil Searching: Compromise and Recompromise of Internet Hosts for Phishing (2009, background): “Moore and Clayton [19] studied the effectiveness of phishing-website removal by recording site lifetimes.”