CyberCorps: Scholarship for Service Program at UofM

Starting from February 2022, I have been leading a new CyberCorps: Scholarship for Service (SFS) program at the University of Memphis (UofM), which aims to increase students’ awareness of cybersecurity opportunities and build a pipeline for careers in government organizations for students in West Tennessee, Mississippi, and Arkansas. Our ultimate goal is to address the growing demand for cybersecurity professionals in the region and ensure that students have the knowledge and skills necessary to thrive in this field. Specifically, we have recruited two CyberCorps SFS cohorts: 1st Cohort (3 graduate students + 4 undergraduate students) and 2nd Cohort (3 graduate students + 3 undergraduate students). In addition to recruitment, we have conducted a series of activities designed to assist SFS scholars in enhancing their practical cybersecurity skills, problem-solving abilities, and overall competencies. This project has the potential to enhance the broader cybersecurity workforce development program at UofM, benefiting not only SFS scholars but also other students interested in pursuing careers in cybersecurity.

For more information about the SFS scholarship and the application procedure, please visit the UofM SFS website.

Media Reports

Congressman Cohen Announces National Science Foundation Cybercorps Scholarships for University of Memphis
UofM gets $3.8M grant from National Science Foundation from ABC24
UofM Receives $3.8M Cybersecurity Education Grant from National Science Foundation

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Designing Machine Learning-based Solutions for APT Detection

This project will explore the recent advancement of machine learning techniques to detect Advance Persistent Threat (APT) attacks. To support efficient and accurate APT detection, the Cybersecurity Intelligence and Analytics (CIA) Group at FedEx has collected large volumes of heterogeneous cybersecurity data from multiple sources within FedEx, including McAfee GW, Win MS, O365, Cisco, DHCP, DNS, etc. With the support of these datasets, the goal of this project is to develop novel APT detection solutions that detect both known attacks (as traditional patterns/signatures-based approaches do) and previously unknown attacks by profiling the normal behavior and detecting attacks as deviations from this normal behavior profile. Collaboratively, the team will first decouple the APT lifecycle into multiple phases and develop effective machine learning-based anomaly detection solutions for alert generation in each phase. Then, deep learning techniques will be applied to extract the correlations among multiple alerts, where attack provenance graphs can be visually summarized. The team will also explore the potential of the next-generation non-relational data management paradigm, e.g., graph database, for cybersecurity research.

Media Reports

Computer Science faculty use FRONTIERS projects to further research impact with FedEx

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