Authentication is the vital link between your real self and your digital self. As our digital selves become ever more powerful, the price of failing authentication grows. The most common authentication protocols are static data and employed only once at login. This allows for authentication to be spoofed just once to gain access to an entire user session. Behaviometric protocols continuously consume a user’s behavior as a token of authentication and can be applied throughout a session, thereby eliminating a fixed token to spoof. Research into these protocols as viable forms of authentication is relatively recent and is being conducted on a variety of data sources, features and classification schemes. This work proposes an extensible research framework to aid the systemization and preservation of research in this field by standardizing the interface for raw data collection, processing and interpretation. Specifically, this framework contributes transparent management of data collection and persistence, the presentation of past research in a highly configurable and extensible form, and the standardization of data forms to enhance innovative reuse and comparative analysis of prior research.
","abstract_html":"<p>Authentication is the vital link between your real self and your digital self. As our digital selves become ever more powerful, the price of failing authentication grows. The most common authentication protocols are static data and employed only once at login. This allows for authentication to be spoofed just once to gain access to an entire user session. Behaviometric protocols continuously consume a user’s behavior as a token of authentication and can be applied throughout a session, thereby eliminating a fixed token to spoof. Research into these protocols as viable forms of authentication is relatively recent and is being conducted on a variety of data sources, features and classification schemes. This work proposes an extensible research framework to aid the systemization and preservation of research in this field by standardizing the interface for raw data collection, processing and interpretation. Specifically, this framework contributes transparent management of data collection and persistence, the presentation of past research in a highly configurable and extensible form, and the standardization of data forms to enhance innovative reuse and comparative analysis of prior research.</p>","abstract_has_math":false,"creators":["Gerrity, Daniel Fleming"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zachary Peterson"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-06-01T07:00:00Z","date_published":"2015-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:42Z","subjects":["authentication","active","framework","security","machine","learning","Computer and Systems Architecture","Other Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2015.71"],"render_values":[{"text":"10.15368/theses.2015.71","href":"https://doi.org/10.15368/theses.2015.71","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1412","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zachary Peterson"]},{"key":"dc:creator","label":"Author","values":["Gerrity, Daniel Fleming"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-06-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["authentication","active","framework","security","machine","learning","Computer and Systems Architecture","Other Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1412","10.15368/theses.2015.71"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Authentication is the vital link between your real self and your digital self. As our digital selves become ever more powerful, the price of failing authentication grows. The most common authentication protocols are static data and employed only once at login. This allows for authentication to be spoofed just once to gain access to an entire user session. Behaviometric protocols continuously consume a user’s behavior as a token of authentication and can be applied throughout a session, thereby eliminating a fixed token to spoof. Research into these protocols as viable forms of authentication is relatively recent and is being conducted on a variety of data sources, features and classification schemes. This work proposes an extensible research framework to aid the systemization and preservation of research in this field by standardizing the interface for raw data collection, processing and interpretation. Specifically, this framework contributes transparent management of data collection and persistence, the presentation of past research in a highly configurable and extensible form, and the standardization of data forms to enhance innovative reuse and comparative analysis of prior research.
"]},{"key":"dc:title","label":"Title","values":["Toward the Systematization of Active Authentication Research"]}]}],"canonical_facts":{"dc:contributor":["Zachary Peterson"],"dc:creator":["Gerrity, Daniel Fleming"],"dc:date.available":["2015-06-10T07:00:00Z"],"dc:description.abstract":["Authentication is the vital link between your real self and your digital self. As our digital selves become ever more powerful, the price of failing authentication grows. The most common authentication protocols are static data and employed only once at login. This allows for authentication to be spoofed just once to gain access to an entire user session. Behaviometric protocols continuously consume a user’s behavior as a token of authentication and can be applied throughout a session, thereby eliminating a fixed token to spoof. Research into these protocols as viable forms of authentication is relatively recent and is being conducted on a variety of data sources, features and classification schemes. This work proposes an extensible research framework to aid the systemization and preservation of research in this field by standardizing the interface for raw data collection, processing and interpretation. Specifically, this framework contributes transparent management of data collection and persistence, the presentation of past research in a highly configurable and extensible form, and the standardization of data forms to enhance innovative reuse and comparative analysis of prior research.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1412","10.15368/theses.2015.71"],"dc:subject":["authentication","active","framework","security","machine","learning","Computer and Systems Architecture","Other Computer Engineering"],"dc:title":["Toward the Systematization of Active Authentication Research"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:42Z"}