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Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

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dc.contributor.authorRathinavel, Gopikrishnaen
dc.contributor.committeechairRamakrishnan, Narendranen
dc.contributor.committeememberLu, Chang Tienen
dc.contributor.committeememberO'Shea, Timothy Jamesen
dc.contributor.committeememberReddy, Chandan K.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2022-06-16T08:00:30Zen
dc.date.available2022-06-16T08:00:30Zen
dc.date.issued2022-06-15en
dc.description.abstractAnomaly 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.en
dc.description.abstractgeneralAnomaly 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.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:34859en
dc.identifier.urihttp://hdl.handle.net/10919/110793en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAnomaly Detectionen
dc.subjectNetwork Data Miningen
dc.subjectGenerative Adversarial Networksen
dc.titleDetecting Irregular Network Activity with Adversarial Learning and Expert Feedbacken
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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