The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features for proper implementation. One way to make disparate data more usable is by using relations within datasets to provide more robust representations of interdependencies.
The purpose of this study is to use machine learning classification algorithms augmented by a new feature set extracted with graph theoretical information representing human to human and human to machine interactions in the quantification of cyber risk due to insider threats. Included in this study is impact assessment by analyzing past incidents caused by internal actors and depicted on the risk matrices, together with datasets on organizational roles of the internal actors.
","abstract_html":"<p>The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features for proper implementation. One way to make disparate data more usable is by using relations within datasets to provide more robust representations of interdependencies.</p> <p>The purpose of this study is to use machine learning classification algorithms augmented by a new feature set extracted with graph theoretical information representing human to human and human to machine interactions in the quantification of cyber risk due to insider threats. Included in this study is impact assessment by analyzing past incidents caused by internal actors and depicted on the risk matrices, together with datasets on organizational roles of the internal actors.</p>","abstract_has_math":false,"creators":["Kucukkaya, Goksel"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Engineering Management & Systems Engineering","degree_department":null,"school":null,"contributors":["C. Ariel Pinto","Adrian Gheorghe","Mustafa Canan","Saltuk Bugra Karahan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-04-01T07:00:00Z","date_published":"2021-04-01T07:00:00Z","updated_at":"2026-07-24T03:35:38Z","subjects":["Cybersecurity","Risk assessment","Anomaly detection","Artificial Intelligence and Robotics","Business Administration, Management, and Operations","Information Security","Risk Analysis","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9798516059070"],"render_values":[{"text":"9798516059070","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/emse_etds/183","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["C. Ariel Pinto","Adrian Gheorghe","Mustafa Canan","Saltuk Bugra Karahan"]},{"key":"dc:creator","label":"Author","values":["Kucukkaya, Goksel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-06-08T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Management & Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cybersecurity","Risk assessment","Anomaly detection","Artificial Intelligence and Robotics","Business Administration, Management, and Operations","Information Security","Risk Analysis","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9798516059070","https://digitalcommons.odu.edu/emse_etds/183"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features for proper implementation. One way to make disparate data more usable is by using relations within datasets to provide more robust representations of interdependencies.
The purpose of this study is to use machine learning classification algorithms augmented by a new feature set extracted with graph theoretical information representing human to human and human to machine interactions in the quantification of cyber risk due to insider threats. Included in this study is impact assessment by analyzing past incidents caused by internal actors and depicted on the risk matrices, together with datasets on organizational roles of the internal actors.
"]},{"key":"dc:title","label":"Title","values":["Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection and Machine Learning"]}]}],"canonical_facts":{"dc:contributor":["C. Ariel Pinto","Adrian Gheorghe","Mustafa Canan","Saltuk Bugra Karahan"],"dc:creator":["Kucukkaya, Goksel"],"dc:date.available":["2023-06-08T07:00:00Z"],"dc:description.abstract":["The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features for proper implementation. One way to make disparate data more usable is by using relations within datasets to provide more robust representations of interdependencies.
The purpose of this study is to use machine learning classification algorithms augmented by a new feature set extracted with graph theoretical information representing human to human and human to machine interactions in the quantification of cyber risk due to insider threats. Included in this study is impact assessment by analyzing past incidents caused by internal actors and depicted on the risk matrices, together with datasets on organizational roles of the internal actors.
"],"dc:identifier":["9798516059070","https://digitalcommons.odu.edu/emse_etds/183"],"dc:subject":["Cybersecurity","Risk assessment","Anomaly detection","Artificial Intelligence and Robotics","Business Administration, Management, and Operations","Information Security","Risk Analysis","Systems Engineering"],"dc:title":["Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection and Machine Learning"],"thesis:degree_discipline":["Engineering Management & Systems Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:38Z"}