Request More Info Apply to ÑÇÉ«Ó°¿â

The Funded Grant

In academic year 2022–23, ÑÇÉ«Ó°¿â received funds in a competitive program, NCAE-C in Cybersecurity Education Research Innovation 2022, that is supported by the National Security Agency to conduct a novel multi-year research project focusing on risk mitigation in zero-trust environments.

Project Description

The title of the funded project is “Innovative Autonomous Risk Detection and Mitigation Powered by Collaborative Machine Learning for Zero-trust Systems.” Its main goal is to develop a new system for automated detection and mitigation of cyber risks to improve the security of computer systems in zero-trust environments. This project introduces a novel framework that uses state-of-the-art collaborative machine learning techniques to develop a suite of adaptive solutions capable of detecting, responding, and mitigating various cyberattacks on networked systems. See the figure at right for the architecture of the proposed collaborative machine learning model to be implemented in this project. The five areas this project focuses on are: 

  • Securing zero-trust networked systems
  • Risk assessment
  • Optimal (near optimal) risk detection procedures
  • Automated risk response
  • Applying collaborative machine learning to build autonomous risk mitigation systems

Architecture of machine learning diagram

Project Outcomes

So far, work on this project has resulted in the following publications/presentations:

  • M. Balega, Waleed Farag, X.W. Wu, S. Ezekiel, and Z. Good, “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics 2024, vol. 13, issue 11, 2148. .
  • A. Weidensall, Waleed Farag, X.W. Wu, S. Ezekiel, et al., “Network Traffic Anomaly Detection Using XGBoost Within a Federated Learning Environment,” A poster presentation at the ÑÇÉ«Ó°¿â Scholars Forum held on April 2024. This poster won the Women in STEM Outstanding Poster Award for Undergraduate Student as well as the Best Computational Science Poster Award (S-COAM).
  • N. Reid,  Waleed Farag, X.W. Wu, S. Ezekiel, et al., “A Comparative Study of the Effectiveness of Various ML Models Used for Autonomously Risk Mitigation,” A poster presentation at the ÑÇÉ«Ó°¿â Scholars Forum held on April 2024.
  • J. Giesen, W. Farag, X-W Wu, S. Ezekiel, et al., "Researching Autonomous Risk Mitigation for Zero-Trust Architectures," A poster presentation at the ÑÇÉ«Ó°¿â Scholars Forum held on April 2023.

Project Meetings and Documents

Waleed Farag, the project director, the two Co-PIs, Drs. Wu and Ezekiel, and all student researchers are holding regular weekly meetings since the beginning of the project in fall 2022. In these meetings, we address outstanding challenges, report of progress, and identify and assign research tasks for the next week. Listed below are detailed meeting documents: