A COMPREHENSIVE EVALUATION OF FEATURE-BASED MALICIOUS WEBSITE DETECTION

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dc.contributor.advisorCukier,, Michelen_US
dc.contributor.authorMcGahagan , John Francisen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-02-13T06:34:25Z
dc.date.available2021-02-13T06:34:25Z
dc.date.issued2020en_US
dc.description.abstractAlthough the internet enables many important functions of modern life, it is also a ground for nefarious activity by malicious actors and cybercriminals. For example, malicious websites facilitate phishing attacks, malware infections, data theft, and disruption. A major component of cybersecurity is to detect and mitigate attacks enabled by malicious websites. Although prior researchers have presented promising results – specifically in the use of website features to detect malicious websites – malicious website detection continues to pose major challenges. This dissertation presents an investigation into feature-based malicious website detection. We conducted six studies on malicious website detection, with a focus on discovering new features for malicious website detection, challenging assumptions of features from prior research, comparing the importance of the features for malicious website detection, building and evaluating detection models over various scenarios, and evaluating malicious website detection models across different datasets and over time. We evaluated this approach on various datasets, including: a dataset composed of several threats from industry; a dataset derived from the Alexa top one million domains and supplemented with open source threat intelligence information; and a dataset consisting of websites gathered repeatedly over time. Results led us to postulate that new, unstudied, features could be incorporated to improve malicious website detection models, since, in many cases, models built with new features outperformed models built from features used in prior research and did so with fewer features. We also found that features discovered using feature selection could be applied to other datasets with minor adjustments. In addition: we demonstrated that the performance of detection models decreased over time; we measured the change of websites in relation to our detection model; and we demonstrated the benefit of re-training in various scenarios.en_US
dc.identifierhttps://doi.org/10.13016/af4t-mdac
dc.identifier.urihttp://hdl.handle.net/1903/26725
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledCybersecurityen_US
dc.subject.pquncontrolledData Scienceen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledNetwork Securityen_US
dc.subject.pquncontrolledThreat Detectionen_US
dc.subject.pquncontrolledWeb Securityen_US
dc.titleA COMPREHENSIVE EVALUATION OF FEATURE-BASED MALICIOUS WEBSITE DETECTIONen_US
dc.typeDissertationen_US

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