Security is critical to the success of software, particularly in today's fast-paced, technology-driven environment. It ensures that data, code, and services maintain their CIA (Confidentiality, Integrity, and Availability). This is only possible if security is taken into account at all stages of the SDLC (Software Development Life Cycle). Various approaches to software quality have been developed, such as CMMI (Capability maturity model integration). However, there exists no explicit solution for incorporating security into all phases of SDLC. One of the major causes of pervasive vulnerabilities is a failure to prioritize security. Even the most proactive companies use the "patch and penetrate" strategy, in which security is accessed once the job is completed. Increased cost, time overrun, not integrating testing and input in SDLC, usage of third-party tools and components, and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC, despite the fact that secure software development is essential for business continuity and survival in today's ICT world. There is a need to implement best practices in SDLC to address security at all levels. To fill this gap, we have provided a detailed overview of secure software development practices while taking care of project costs and deadlines. We proposed a secure SDLC framework based on the identified practices, which integrates the best security practices in various SDLC phases. A mathematical model is used to validate the proposed framework. A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC, resulting in more secure applications.

Cancer is a complicated global health concern with a significant fatality rate. Breast cancer is among the leading causes of mortality each year. Advancements in prognoses have been progressively based primarily on the expression of genes, offering insight into robust and appropriate healthcare decisions, owing to the fast growth of advanced throughput sequencing techniques and the use of various deep learning approaches that have arisen in the past few years. Diagnostic-imaging disease indicators such as breast density and tissue texture are widely used by physicians and automated technology. The effective and specific identification of cancer risk presence can be used to inform tailored screening and preventive decisions. For several classifications and prediction applications, such as breast imaging, deep learning has increasingly emerged as an effective method. We present a deep learning model approach for predicting breast cancer risk primarily on this foundation. The proposed methodology is based on transfer learning using the InceptionResNetV2 deep learning model. Our experimental work on a breast cancer dataset demonstrates high model performance, with 91% accuracy. The proposed model includes risk markers that are used to improve breast cancer risk assessment scores and presents promising results compared to existing approaches. Deep learning models include risk markers that are used to improve accuracy scores. This article depicts breast cancer risk indicators, defines the proper usage, features, and limits of each risk forecasting model, and examines the increasing role of deep learning (DL) in risk detection. The proposed model could potentially be used to automate various types of medical imaging techniques.

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