Safwa Ameer
Details
2023 : Present
MITRE
Senior Cybersecurity Engineer
National Science Foundation (NSF) Postdoctoral research fellowship awardee.
Investigating :
Foundational Cyber and Computer Security.
Security and Privacy in IoT systems.
Access Control Models Analysis, design, and Enforcement.
Enforcing zero trust principles in cloud enabled IoT systems.
Deep learning based access control.
2021 : 2023
The University of Texas at San Antonio
Postdoctoral Researcher
Internship in bioinformatics at the Greehey Children’s Cancer Research Institute (GCCRI) at UTHSCSA.
Used algorithms and machine learning concepts and tools to solve bioinformatics problems.
2017 : 2017
UT Health San Antonio
Student Intern
2016 : 2017
The University of Texas at San Antonio
Research Assistant
Grader at the Computer Science department
2016 : 2017
The University of Texas at San Antonio
Grader
About
- Member of MITRE’s highly selective National Security Accelerator Program (NSAP). The program accelerates the career of highly motivated, early career candidates passionate about solving problems for a safer world! The program offers unique onboarding, mentoring, and work opportunities to build skills as a future leader within the national security community.
- NSF Postdoctoral Research Fellowship (NSF-PRF) awardee.
- I was a visiting researcher at the Institute for Cyber Security (ICS) at The University of Texas at San Antonio.
Recent Research Experience:
1- Access Control: My research primarily centers on the foundational aspects of authorization. This encompasses developing access control models, conducting security analyses, and exploring the applications of these technologies in various domains, including cloud-enabled IoT systems.
2- Zero Trust: My research focuses on investigating the requirements of Zero Trust (ZT) paradigm and proposing frameworks and models that effectively enforce this paradigm across different application domains, including IoT systems. In particular, my work centers on identifying the key components of a ZT architecture and designing solutions that integrate these components into existing systems.
3- Deep Learning-Based Access Control Toward Resilient Access Control Systems: I am currently exploring the development of deep learning-based access control models. The aim is to develop attack-aware (resilient) access control models capable of detecting threats and reacting accordingly. This research involves designing and implementing machine learning algorithms and experimenting with large datasets to assess their effectiveness.
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