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A Firefly-Optimized Secure Software Development Framework for Medical Data Using Machine Learning Techniques
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Keywords
Secure SDLC
Firefly Optimization
Machine Learning
Medical Data
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Abstract
The increasing adoption of digital healthcare systems has highlighted critical vulnerabilities in medical data handling and software development practices. Ensuring security across the Software Development Life Cycle (SDLC) is essential for safeguarding sensitive patient information. This study proposes a Firefly-Optimized Secure Software Development Framework (FSSDF) that integrates machine learning techniques to enhance threat detection and system resilience in medical software. The framework leverages the Firefly Optimization Algorithm to systematically prioritize security measures across SDLC stages, including requirement analysis, design, implementation, testing, and deployment. Using a synthesis of existing research, this study identifies critical vulnerabilities, evaluates optimization strategies, and demonstrates the feasibility of machine learning-assisted secure SDLC in healthcare applications. Findings indicate that the proposed framework improves detection rates of security anomalies while reducing development overhead, thereby providing a scalable and efficient approach to secure medical software engineering.
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