Anomaly Based Web Phishing Page Detection

By Yingyu Pan, Xuhua Ding. 2006

240 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

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In Signal Processing

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): “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.”
There Is No Free Phish: An Analysis of "Free" and Live Phishing Kits (2008, methodology): “The detection of spoofed web sites has also received considerable attention, and various techniques have been proposed, based, for example, on the measurement of visual similarity between web pages [31], anomaly detection techniques [22], or information retrieval approaches [35].”
An Empirical Analysis of Phishing Blacklists (2009, methodology): “proposed a method to compile a list of phishing webpage features by extracting selected DOM properties of the webpage, such as the page title, meta description field, etc [29].”
Malicious URL Detection using Machine Learning: A Survey (2017, background): “Speciﬁcally, let P ( x | y ) denote the conditional probability of the feature vector given a class, the independence assumption implies that P ( x | y ) = Π di =1 P ( x i | y ) , where d is the number of features.”
Phishing detection based Associative Classification data mining (2014, methodology): “A method based on SVM was proposed in Pan and Ding (2006) to discover unusual activities, i.e. phishing, in websites based on two variables.”
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”
Predicting phishing websites based on self-structuring neural network (2013, methodology): “Another method proposed in [20] suggested a way to detect phishing websites by capturing abnormal behaviours demonstrated by these websites.”
Examining the impact of website take-down on phishing (2007, background): “Categories and Subject Descriptors K.4.4 [Computing Milieux]: Computers and Society Electronic Commerce, Security Keywords phishing, security economics, electronic crime 1.”