E. Bowen
23 Papers
E. Bowen is an academic researcher. The author has contributed to research in topics: Computer science & Health care. The author has an hindex of 2, co-authored 12 publications.
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Papers
Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions
TL;DR: In this paper , the authors applied various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset and found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration.
Discovering Command and Control Channels Using Reinforcement Learning
Cheng Wang,A. K. Kakkar,Christopher Samuel Redino,Abdul Rahman,Ryan Clark,Daniel Radke,Tyler Cody,Lanxiao Huang,E. Bowen +8 more
- 01 Apr 2023
TL;DR: In this article , a reinforcement learning-based approach is used to automatically carry out C2 attack campaigns on large networks, where multiple defense layers are in place, and the objective is to maximize the number of valuable hosts whose data is exfiltrated.
4
Zero Day Threat Detection Using Metric Learning Autoencoders
Dhruv Nandakumar,Robert Schiller,Christopher Samuel Redino,K. Choi,A. S. M Mus qur Rahman,E. Bowen,Marc Vucovich,Joe Nehila,M. Weeks,Aaron Shaha +9 more
- 01 Nov 2022
TL;DR: In this paper , the authors demonstrate an improvement upon a previously introduced methodology, which used a dual-autoencoder approach to identify zero-day threats (ZDTs) in network flow telemetry.
3
Lateral Movement Detection Using User Behavioral Analysis
Deepak Baijusharan Kushwaha,Dhruv Nandakumar,Akshay Kakkar,Sanvi Gupta,K. Choi,Christopher Samuel Redino,A. S. M Mus qur Rahman,Sabthagiri Saravanan Chandramohan,E. Bowen,M. Weeks,Aaron Shaha,Joe Nehila +11 more
TL;DR: A computationally efficient approach to near real-time Lateral Movement detection that is interpretable and robust to enterprise-scale data volumes and class imbalance is provided.
1
Foundational Models for Malware Embeddings Using Spatio-Temporal Parallel Convolutional Networks
Dhruv Nandakumar,Christopher Samuel Redino,K. Choi,Abdul Rahman,E. Bowen +4 more
TL;DR: In this article , the authors introduce a novel method that combines convolutional neural networks, standard graph embedding techniques, and a metric learning objective to extract meaningful information from network flow data and create strong embeddings characterizing malware behavior.