Machine Learning (ML) holds a pivotal position in the realm of cyber defense, particularly given the expanding scale of networks, the proliferation of software and malware, and the deluge of data they generate. One of the paramount challenges faced by cyber defenders is the ability to differentiate between malicious anomalies and benign yet uncommon activities. This task has taken on heightened significance as the attack surfaces within large enterprise networks continue to expand. In this context, anomaly detection systems grounded in statistical and large-scale analysis/modeling of user and device behavior have emerged as indispensable tools for identifying and mitigating malicious activities.
Papers
- Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection.,
M. Eren, M. Bhattarai, R. J. Joyce, E. Raff, C. Nicholas, B. S. Alexandrov
ACM Transactions on Privacy and Security: 2023. - Robust Adversarial Defense by Tensor Factorization.,
M. Bhattarai, M. C. Kaymak, R. Barron, B. Nebgen, K. Rasmussen, ...
arXiv preprint arXiv:2309.01077: 2023. - One-Shot Federated Group Collaborative Filtering.,
M. E. Eren, M. Bhattarai, N. Solovyev, L. E. Richards, R. Yus, C. Nicholas, ...
IEEE International Conference on Machine Learning and Applications: 2022. - Fedsplit: One-shot federated recommendation system based on non-negative joint matrix factorization and knowledge distillation.,
M. E. Eren, L. E. Richards, M. Bhattarai, R. Yus, C. Nicholas, B. S. Alexandrov
arXiv preprint arXiv:2205.02359: 2022. - Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization,
M. Eren, J. Moore, and B.S. Alexandrov.
Proceedings of 18th IEEE International Conference on Intelligence and Security Informatics (ISI), Nov. 9-10, 2020.