{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/110793"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/110793","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback","abstract":"Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection.","abstract_html":"Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection.","abstract_has_math":false,"creators":["Rathinavel, Gopikrishna"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Ramakrishnan, Narendran"],"committee_members":["Lu, Chang Tien","O'Shea, Timothy James","Reddy, Chandan K."],"year":2022,"date_issued":"2022-06-15","date_published":"2022-06-15","updated_at":"2026-07-22T22:19:47Z","subjects":["Anomaly Detection","Network Data Mining","Generative Adversarial Networks"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34859"],"render_values":[{"text":"vt_gsexam:34859","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/110793","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Ramakrishnan, Narendran"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lu, Chang Tien","O'Shea, Timothy James","Reddy, Chandan K."]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Rathinavel, Gopikrishna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-06-16T08:00:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-16T08:00:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-06-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Anomaly Detection","Network Data Mining","Generative Adversarial Networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34859"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/110793"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Anomaly detection is a technique that can be used to detect if there is any abnormal behavior in data. It is a ubiquitous and a challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. Anomaly detection in such communication networks is essential in ensuring security. This thesis proposes a novel framework CAAD for anomaly detection in wireless communication systems. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84% over state-of-the-art anomaly detection models. Additionally, CAAD is augmented with the ability to incorporate feedback from experts about whether a sample is normal or anomalous through a novel feedback loop (CAAD-EF). CAAD-EF is a novel, holistic and a widely applicable solution to anomaly detection."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Ramakrishnan, Narendran"],"dc:contributor.committeemember":["Lu, Chang Tien","O'Shea, Timothy James","Reddy, Chandan K."],"dc:contributor.department":["Computer Science"],"dc:creator":["Rathinavel, Gopikrishna"],"dc:date.accessioned":["2022-06-16T08:00:30Z"],"dc:date.available":["2022-06-16T08:00:30Z"],"dc:date.issued":["2022-06-15"],"dc:description.abstract":["Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection."],"dc:description.abstractgeneral":["Anomaly detection is a technique that can be used to detect if there is any abnormal behavior in data. It is a ubiquitous and a challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. Anomaly detection in such communication networks is essential in ensuring security. This thesis proposes a novel framework CAAD for anomaly detection in wireless communication systems. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84% over state-of-the-art anomaly detection models. Additionally, CAAD is augmented with the ability to incorporate feedback from experts about whether a sample is normal or anomalous through a novel feedback loop (CAAD-EF). CAAD-EF is a novel, holistic and a widely applicable solution to anomaly detection."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:34859"],"dc:identifier.uri":["http://hdl.handle.net/10919/110793"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Anomaly Detection","Network Data Mining","Generative Adversarial Networks"],"dc:title":["Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:47Z"}