Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden communication channel that is difficult to detect. In the following research, a technique is proposed to detect/classify the type of privileged information leaked over a covert timing channel, using communication packets and a machine learning algorithm to train a classification model to identify the origin of a data breach.
","abstract_html":"<p>Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden communication channel that is difficult to detect. In the following research, a technique is proposed to detect/classify the type of privileged information leaked over a covert timing channel, using communication packets and a machine learning algorithm to train a classification model to identify the origin of a data breach.</p>","abstract_has_math":false,"creators":["Frisbie, Gayle L."],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Open Access Thesis","degree_discipline":"College of Engineering and Technology","degree_department":null,"school":null,"contributors":["Omar Darwish, Ph.D.","Munther Abualkibash, Ph.D.","Anas Alsobeh, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01T08:00:00Z","date_published":"2024-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:47Z","subjects":["covert timing channel","machine learning","binary translation","data type origin detection","supervised learning","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/1231","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Omar Darwish, Ph.D.","Munther Abualkibash, Ph.D.","Anas Alsobeh, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Frisbie, Gayle L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-14T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["College of Engineering and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["covert timing channel","machine learning","binary translation","data type origin detection","supervised learning","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/1231"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden communication channel that is difficult to detect. In the following research, a technique is proposed to detect/classify the type of privileged information leaked over a covert timing channel, using communication packets and a machine learning algorithm to train a classification model to identify the origin of a data breach.
"]},{"key":"dc:title","label":"Title","values":["Identifying the origins of business’ data breaches utilizing covert timing channels"]}]}],"canonical_facts":{"dc:contributor":["Omar Darwish, Ph.D.","Munther Abualkibash, Ph.D.","Anas Alsobeh, Ph.D."],"dc:creator":["Frisbie, Gayle L."],"dc:date.available":["2024-06-14T07:00:00Z"],"dc:description.abstract":["Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden communication channel that is difficult to detect. In the following research, a technique is proposed to detect/classify the type of privileged information leaked over a covert timing channel, using communication packets and a machine learning algorithm to train a classification model to identify the origin of a data breach.
"],"dc:identifier":["https://commons.emich.edu/theses/1231"],"dc:subject":["covert timing channel","machine learning","binary translation","data type origin detection","supervised learning","Computer Sciences"],"dc:title":["Identifying the origins of business’ data breaches utilizing covert timing channels"],"thesis:degree_discipline":["College of Engineering and Technology"],"thesis:degree_level":["Open Access Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T02:17:47Z"}