Marie Louise Uwibambe
Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Marie Louise Uwibambe's research focuses on enhancing the security of industrial control systems and operational technology environments. Her work investigates the application of fuzzing techniques for vulnerability discovery in these critical systems, particularly in programmable logic controllers (PLCs). Uwibambe has explored methods for detecting silent crashes in control devices and optimizing the selection of vulnerability remediation actions. She has also examined the integration of artificial intelligence, specifically reinforcement learning, for input mutation in fuzzing processes. Additionally, her research has touched upon the implications of large language models, such as ChatGPT, in the context of vulnerability management, highlighting both benefits and drawbacks.
Uwibambe collaborates with researchers at the University of Arkansas at Fayetteville, including Qinghua Li, Kylie McClanahan, Yanjun Pan, and Sky Elder, with whom she has co-authored multiple publications. Her scholarship includes six publications with an h-index of 2 and 13 total citations.
Metrics
- h-index: 2
- Publications: 6
- Citations: 15
Selected Publications
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Vulnerability Discovery In Industrial Control Systems Using Fuzzing (2025)Journal of the Arkansas Academy of Science OpenAlex
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Optimizing the Selection of Vulnerability Remediation Actions in Operational Technology Environments (2025)
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A Reinforcement Learning Approach to Multi-Parametric Input Mutation for Fuzzing (2025)
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Safeguarding Industrial Automation: A Fuzzing Framework for PLC Control Logic (2025)
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When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly (2024)
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Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices (2023)
Collaboration Network
Top Collaborators
- When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly
- Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices
- A Reinforcement Learning Approach to Multi-Parametric Input Mutation for Fuzzing
- Optimizing the Selection of Vulnerability Remediation Actions in Operational Technology Environments
- Safeguarding Industrial Automation: A Fuzzing Framework for PLC Control Logic
- When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly
- Optimizing the Selection of Vulnerability Remediation Actions in Operational Technology Environments
- Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices
- When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly
- When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly
- When ChatGPT Meets Vulnerability Management: The Good, the Bad, and the Ugly
- A Reinforcement Learning Approach to Multi-Parametric Input Mutation for Fuzzing
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