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Insider Threat Detection Data Augmentation Using WCGAN-GP

dc.contributor.authorPreston, Mack
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Michael McAllisteren_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Xiao Luoen_US
dc.contributor.thesis-readerDr. Malcolm Heywooden_US
dc.contributor.thesis-supervisorDr. Nur Zincir-Heywooden_US
dc.date.accessioned2022-04-12T14:09:22Z
dc.date.available2022-04-12T14:09:22Z
dc.date.defence2022-04-11
dc.date.issued2022-04-12T14:09:22Z
dc.description.abstractThis thesis explores the application of Generative Adversarial Networks (GANs) in augmenting insider threat detection datasets to alleviate class imbalance. In addition, a machine learning based insider threat detection system is proposed that augments datasets to improve detection rates while maintaining precision. WCGAN-GP, a promising new GAN variant, is trained on a publicly available synthetic insider threat dataset and used to generate realistic samples for multiple insider scenarios. The generated samples are used to augment the dataset, which is then used to train supervised classifiers to detect insider threats. The WCGAN-GP based augmentation strategy outperforms the baseline (under-sampled) strategy on a large feature set, increasing the detection rate while preserving a low false-positive rate. The framework was further tested on two later versions of the dataset which contain modified behaviour and new insider scenarios. The results show that the proposed approach is robust and can generalize to novel insider threat scenarios.en_US
dc.identifier.urihttp://hdl.handle.net/10222/81531
dc.language.isoenen_US
dc.subjectInsider Threat Detectionen_US
dc.subjectData Augmentationen_US
dc.subjectWCGAN-GPen_US
dc.subjectGANen_US
dc.subjectMachine Learningen_US
dc.subjectCyber Securityen_US
dc.titleInsider Threat Detection Data Augmentation Using WCGAN-GPen_US

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