Paypal, Inc.

Nicholas Bailey

Bradley Wardman

Nathan Robert Pratt

Meethil Vijay Yadav

Kevin Tyers

Eric Nunes

Todd Clausen

A method for phishing detection based on modeling of web page content is discussed. The method includes accessing suspect web page content of a suspect Uniform Resource Locator (URL). The method includes generating an exemplary model based on an exemplary configuration for an indicated domain associated with the suspect URL, where the exemplary model indicates structure and characteristics of an example web page of the indicated domain. The method includes generating a suspect web page model that indicates structure and characteristics of the suspect web page content. The method includes performing scoring functions for the potential phishing web page content based on the suspect web page model, where some of the scoring functions use the exemplary model to perform analysis to generate respective results. The method includes generating a web page content phishing score based on results from the scoring functions.

Phishing detection based on modeling of web page content

A method for phishing detection using uniform resource locators is discussed. The method includes accessing data from one or more of a monitored portion of website data and a monitored portion of emails, the data indicating a suspect Uniform Resource Locator (URL). The method includes assigning a rule score based on partial rule scores of each portion of the suspect URL, the rule score indicating a phishing potential based on URL rules. The method includes determining a uniqueness score of the suspect URL, the uniqueness score indicating a degree of uniqueness of the suspect URL from a plurality of known phishing URLs. The method also includes determining a URL phishing score based, at least in part, on the rules scores and the uniqueness score for the suspect URL.

Phishing detection using uniform resource locators

A method for using a malware and phishing detection and mediation platform is discussed. The method includes accessing data from one or more of a monitored portion of website data and a monitored portion of emails, the data indicating a respective potential malware or a suspect phishing element (e.g., Uniform Resource Locator (URL)). The method includes selecting one of a plurality of detection engines for processing the data, where the selecting is based on previous results of previous processing by one or more detection engines. Each of the plurality of detection engines can be for performing one or more respective investigation actions on the plurality of data to determine a particular issue with one of the monitored data. The method also includes determining a mediation action based on a result of processing of the detection engine and the previous processing.

Malware and phishing detection and mediation platform

A method for phishing detection using certificates associated with uniform resource locators (URLs) is discussed. The method includes accessing certificate portions of a certificate associated with a suspect URL, the certificate accessed at a database that includes certificates obtained by monitoring certificate logs. The method includes accessing a URL score for the suspect URL. The method includes assigning a certificate rule score based on partial certificate scores of certificate portions, the certificate rule score indicating a phishing potential for the certificate, each of the partial certificate scores indicating a likelihood of phishing of each portion based on certificate rules. The method includes using a machine learning model based on the URL score and the certificate to determine a uniqueness certificate score. The method also includes determining a phishing certificate score based on the certificate rule score and the uniqueness certificate score for the certificate.

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Mesa, AZ, United States of America

Nicholas Bailey