A framework for detection and measurement of phishing attacks

By Sujata Garera, Niels Provos, Monica Chew, Aviel D. Rubin. 2007

564 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, neutral): “These approaches exploit features computed from the webpage 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…”
PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, methodology): “Most phishing detection techniques identify phishing pages by building a classifier using different heuristics based on URL features [13, 24] or on the web page content [35, 45, 41].”
Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, background): “pdf significant percentage of IP address in the URL pathname [18].”
Building a Dynamic Reputation System for DNS (2010, background): “Garera et al. [8] discussed “phishing” detection predominately using properties of the URL and not statistical observations about the domains or the IP address.”
Prophiler: a fast filter for the large-scale detection of malicious web pages (2011, unknown): “Analyzing the URLs of several pages launching drive-by-download exploits, we observed that many of them shared common names or recurring patterns in their paths (we speculate that this is an indication that different attacks are performed using the same exploit toolkits, e.g., MPack, Eleonore, and C”
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”
A Hierarchical Adaptive Probabilistic Approach for Zero Hour Phish Detection (2010, background): “Garera et al [13] identified a set of fine-grained heuristics from URLs, and combined them with other features to detect phish.”
Proactive Discovery of Phishing Related Domain Names (2012, background): “Moreover, Garera et al. , in [8], are the ﬁrst to use the occurrences of manually deﬁned words (secure, banking, login, etc.) in URL as features.”
An Empirical Analysis of Phishing Blacklists (2009, background): “identified a set of finegrained heuristics from phishing URLs alone [13].”
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.”