LatentStruct-ID: Latent Structural Learning for Unsupervised Synthetic Identity Discovery
Keywords:
Synthetic identity discovery, latent structural learning, unsupervised learning, identity analytics, anomaly detection, structural modeling, digital ecosystems.Abstract
Detecting synthetic identities in operational systems is challenging when labels are unavailable, andattackers employ complex, multi-step manipulations. This research introduces LatentStruct-ID, anunsupervised framework that leverages latent structural learning to discover synthetic identities in large scale multi-modal datasets
References
Arner, D. W., Zetzsche, D. A., Buckley, R. P., and Barberis, J. N. The identity challenge in finance: From analogue identity to digitized identification to digital KYC utilities. European Business Organization Law Review, 2019, 20(1): 55–80.
Jain, A. K., and Ross, A. Bridging the gap between biometrics and forensics. Philosophical Transactions of the Royal Society B, 2015.
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Published
2021-03-20
How to Cite
Suman Kumar Sanjeev Prasanna. (2021). LatentStruct-ID: Latent Structural Learning for Unsupervised Synthetic Identity Discovery . Journal of Computational Analysis and Applications (JoCAAA), 29(3), 634–646. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5282
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