Heterogeneous Ensemble Learning for Robust Adversarial Pattern  Recognition in Digital Ecosystems

Suman Kumar Sanjeev Prasanna

Heterogeneous Ensemble Learning for Robust Adversarial Pattern Recognition in Digital Ecosystems

Authors

Suman Kumar Sanjeev Prasanna

Keywords:

Digital identity, anomaly detection, ensemble learning, machine learning, security.

Abstract

Digital identity ecosystems increasingly rely on high-dimensional, heterogeneous behavioral andtransactional data, exposing critical infrastructures to subtle and dynamic adversarial behaviors.Traditional single-model detection methods, including standalone decision trees, gradient-based models, or linear classifiers, frequently

References

G. Wolfond, “A Blockchain Ecosystem for Digital Identity: Improving Service Delivery in Canada’s Public and Private Sectors,” Technol. Innov. Manag. Rev., vol. 7, no. 10, pp. 35–40, 2017, doi: 10.22215/timreview/1112.

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Published

2019-05-15

How to Cite

Suman Kumar Sanjeev Prasanna. (2019). Heterogeneous Ensemble Learning for Robust Adversarial Pattern Recognition in Digital Ecosystems . Journal of Computational Analysis and Applications (JoCAAA), 27(5), 18–28. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4972