{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79353"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79353","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Understanding the Phish: Using Judgment Analysis to Evaluate the Human Judgment of Phishing Emails","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Molinaro, Kylie; 0000-0002-2606-4243"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bolton, Matthew","Industrial and Systems Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-04-04T20:30:44Z","date_published":"2019-04-04T20:30:44Z","updated_at":"2026-07-27T19:05:16Z","subjects":["industrial engineering","cognitive psychology"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79353","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bolton, Matthew","Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Molinaro, Kylie; 0000-0002-2606-4243"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:30:44Z","2019","2018-12-17 10:08:41"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["industrial engineering","cognitive psychology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79353"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Phishing emails, malicious messages designed to appear legitimate in an attempt to get individuals to conduct compromising actions, pose a continuously growing threat to cybersecurity. Phishing campaigns are responsible for around 90% of all identified data breaches and result in billions of dollars lost each year. Existing user training and automatic filtering techniques are not grounded in cognitive theory and thus have limited effectiveness. As such, there is a real need to understand how users synthesize information to identify phishing emails. The lens model, a judgment analysis (JA) technique, uses symmetric statistical models of the environment (also called the criterion) and the judgment values made by the human to evaluate human judgment performance. Because the lens model provides a means of analyzing both the environment and the human users, it was hypothesized that it would be a more effective way of understanding the phishing problem than conventional approaches. Further, recent literature suggests that cognitive automaticity plays a critical role in phishing victimization. The overlap between the lens model and the cognitive continuum theory (CCT; a human judgment theory that places cognitive modes along a continuum from intuitive to analytical cognition) enables the effect of automaticity (intuitive cognition in CCT terms) on phishing detection to be studied at a higher fidelity than was previously possible. This research focused on applying the lens model and the CCT to phishing. This aimed to satisfy three objectives: validate the lens model approach for the analysis of phishing email judgments, explore the differences in lens model approaches within this domain, and apply and extend the lens model’s analysis capabilities with the CCT to better understand the phishing problem."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Understanding the Phish: Using Judgment Analysis to Evaluate the Human Judgment of Phishing Emails"]}]}],"canonical_facts":{"dc:contributor":["Bolton, Matthew","Industrial and Systems Engineering"],"dc:creator":["Molinaro, Kylie; 0000-0002-2606-4243"],"dc:date":["2019-04-04T20:30:44Z","2019","2018-12-17 10:08:41"],"dc:description":["Ph.D.","Phishing emails, malicious messages designed to appear legitimate in an attempt to get individuals to conduct compromising actions, pose a continuously growing threat to cybersecurity. Phishing campaigns are responsible for around 90% of all identified data breaches and result in billions of dollars lost each year. Existing user training and automatic filtering techniques are not grounded in cognitive theory and thus have limited effectiveness. As such, there is a real need to understand how users synthesize information to identify phishing emails. The lens model, a judgment analysis (JA) technique, uses symmetric statistical models of the environment (also called the criterion) and the judgment values made by the human to evaluate human judgment performance. Because the lens model provides a means of analyzing both the environment and the human users, it was hypothesized that it would be a more effective way of understanding the phishing problem than conventional approaches. Further, recent literature suggests that cognitive automaticity plays a critical role in phishing victimization. The overlap between the lens model and the cognitive continuum theory (CCT; a human judgment theory that places cognitive modes along a continuum from intuitive to analytical cognition) enables the effect of automaticity (intuitive cognition in CCT terms) on phishing detection to be studied at a higher fidelity than was previously possible. This research focused on applying the lens model and the CCT to phishing. This aimed to satisfy three objectives: validate the lens model approach for the analysis of phishing email judgments, explore the differences in lens model approaches within this domain, and apply and extend the lens model’s analysis capabilities with the CCT to better understand the phishing problem."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79353"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["industrial engineering","cognitive psychology"],"dc:title":["Understanding the Phish: Using Judgment Analysis to Evaluate the Human Judgment of Phishing Emails"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:16Z"}