A Secure Aware SDLC with Hybrid Model of Artificial Neural Network-Firefly Optimization Algorithm for Estimating Security in Medical Software
The medical data requires an increase in use of security driven approaches to support software development activities, such as requirements, design and implementation. The main idea for practicing research towards security is to maintain usability of the software as well that was achieved by making less complex and high secure software. In order to meet our goal, we conducted a systematic mapping study to identify the primary studies on the use of software security techniques in Software Development Life Cycle (SDLC). The present research is intended to estimate the usable-security of software and achieves an objective of developing software with optimum security while retaining its usability. The decision-makers often find it difficult to integrate security and usability and therefore the present research hybrid Artificial Neural Network-Firefly Optimization Algorithm (ANN-FOA) work introduces an approach that integrates usability and security with its contributing attributes. The present research hybrid ANN-FOA significantly assess the usability along with security for SDLC model for medical field. The present hybrid ANN-FOA system introduces a procedural sensitivity was also achieved by using the various versions of the method. The findings of the usability along with security assessment insist that this inventive hybrid procedure would be the most conversant mechanism for determining the usable-security of software. Further these findings will be helpful in managing security without affecting the usability for end user.