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Full-Text Articles in Cybersecurity
Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf
Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf
Electrical & Computer Engineering Projects for D. Eng. Degree
As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …
A.I.R.E., Laurene Robinson
A.I.R.E., Laurene Robinson
Presentations - 2026
•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly
A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson
A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson
Posters - 2026
Reverse engineering plays a vital role in cybersecurity by helping analysts examine unknown binaries, investigate malware, identify vulnerabilities, and better protect sensitive systems. However, once a program is compiled and stripped, the meaningful names that describe its behavior are lost, leaving behind generic function labels like FUN_00401a30. Analysts must then manually interpret decompiled code, trace call chains, and infer program behavior function by function, which is slow and mentally demanding on large binaries. To address this challenge, this project introduces A.I.R.E., a local Ghidra extension that extracts contextual evidence from stripped functions and uses a locally hosted language model to …
Dsa-Api: Data Standardization Automation Using Ai-Powered Apis, Andrew Asher Turner
Dsa-Api: Data Standardization Automation Using Ai-Powered Apis, Andrew Asher Turner
Master's Theses
The common factor with current implementations of Artificial Intelligence (AI) is data. Companies are constantly looking for new ways to analyze data, but it comes in various formats: Text, Comma Separated Value (CVE), JavaScript Object Notation (JSON), Extensible Markup Language (XML), and Excel. How can AI be adapted to standardize formats for data analysis, integration, and digestion efficiently? Published research acknowledged that Machine Learning (ML) and AI can provide an automated method to speed up this process and limit the human decision-making error. With the advancement in AI, Application Programming Interfaces (APIs) prompt the idea that they can take in …