Security Threat and Vulnerability Assessment and Measurement in Secure Software Development

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(Capabilitymaturitymodel 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,inwhich 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.

Multi-Scale Network for Thoracic Organs Segmentation

Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately.