Development of Advanced AI and Deep Learning Models for Security Patches
| Reference number | |
| Coordinator | Högskolan i Halmstad |
| Funding from Vinnova | SEK 145 000 |
| Project duration | October 2024 - March 2025 |
| Status | Ongoing |
| Venture | International individual mobility within cutting-edge technology |
| Call | Closed offer - International individual mobility for cutting-edge technology 2024 |
Purpose and goal
By aligning research efforts with the global goals outlined in Agenda 2030, the project seeks to address critical challenges in system security and AI. Specifically, the project focuses on enhancing security systems by utilizing advanced AI techniques to mitigate bias, particularly gender bias, within machine learning models.
Expected effects and result
The project aims to advance AI capabilities by integrating hidden security patch collection techniques and vulnerability prediction methods. The key objective of this research is to develop robust models capable of identifying and predicting vulnerabilities in open-source software.
Planned approach and implementation
The results promise to enhance the effectiveness and reliability of software security systems, providing more secure and resilient AI-driven solutions.