Sadia Nasrin Tisha
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sadia Nasrin Tisha has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Shahidur Rahaman
Department of Computer Science, ECS, Baylor University, Waco, TX 76798, USA
2 shared publications - Eunjee Song
Department of Computer Science, Baylor University, Waco, TX, USA
1 shared publication - Geethanjali Nallani
University of Maryland, Baltimore County
1 shared publication - Javier A. Orduz-Ducuara
Earlham College
1 shared publication - Mushfika Sharmin Rahman
Baylor University
1 shared publication - Nazmus Sakib
University of Maryland, Baltimore County, Baltimore, MD, USA
1 shared publication
Latest Publications
- Open Access
- 17 February 2026
- Published by Association for Computing Machinery (ACM)
- p. 1061-1067
Competing or Collaborating? The Role of Hackathon Formats in Shaping Team Dynamics and Project Choices
- 17 February 2026
- Published by Association for Computing Machinery (ACM)
- p. 1061-1067
Open AccessCompeting or Collaborating? The Role of Hackathon Formats in Shaping Team Dynamics and Project Choices
- 15 August 2025
- Published by Springer Nature
- p. 58-66
Quantum Machine Learning for Heart Disease Detection: A Case Study
- 15 August 2025
- Published by Springer Nature
- p. 58-66
Quantum Machine Learning for Heart Disease Detection: A Case Study
- 22 January 2025
- Published by Springer Nature
- p. 255-266
Enhancing Classification of Aquatic Species Through Supervised Contrastive Learning and Advanced Image Super-Resolution
- 22 January 2025
- Published by Springer Nature
- p. 255-266
Enhancing Classification of Aquatic Species Through Supervised Contrastive Learning and Advanced Image Super-Resolution
- 27 June 2024
- Published by Springer Nature
- p. 184-196
A Comparative Evaluation of Image Caption Synthesis Using Deep Neural Network
- 27 June 2024
- Published by Springer Nature
- p. 184-196
A Comparative Evaluation of Image Caption Synthesis Using Deep Neural Network
- Open Access
Access Control Design Practice and Solutions in Cloud-Native Architecture: A Systematic Mapping Study
Protecting the resources of a cloud-native application is essential to meet an organization’s security goals. Cloud-native applications manage thousands of user requests, and an organization must employ a proper access control mechanism. However, unfortunately, developers sometimes grumble when designing and enforcing access decisions for a gigantic scalable application. It is sometimes complicated to choose the potential access control model for the system. Cloud-native software architecture has become an integral part of the industry to manage and maintain customer needs. A microservice is a combination of small independent services that might have hundreds of parts, where the developers must protect the individual services. An efficient access control model can defend the respective services and consistency. This study intends to comprehensively analyze the current access control mechanism and techniques utilized in cloud-native architecture. For this, we present a systematic mapping study that extracts current approaches, categorizes access control patterns, and provides developers guidance to meet security principles. In addition, we have gathered 234 essential articles, of which 29 have been chosen as primary studies. Our comprehensive analysis will guide practitioners to identify proper access control mechanisms applicable to ensuring security goals in cloud-native architectures.
Open AccessAccess Control Design Practice and Solutions in Cloud-Native Architecture: A Systematic Mapping Study
Protecting the resources of a cloud-native application is essential to meet an organization’s security goals. Cloud-native applications manage thousands of user requests, and an organization must employ a proper access control mechanism. However, unfortunately, developers sometimes grumble when designing and enforcing access decisions for a gigantic scalable application. It is sometimes complicated to choose the potential access control model for the system. Cloud-native software architecture has become an integral part of the industry to manage and maintain customer needs. A microservice is a combination of small independent services that might have hundreds of parts, where the developers must protect the individual services. An efficient access control model can defend the respective services and consistency. This study intends to comprehensively analyze the current access control mechanism and techniques utilized in cloud-native architecture. For this, we present a systematic mapping study that extracts current approaches, categorizes access control patterns, and provides developers guidance to meet security principles. In addition, we have gathered 234 essential articles, of which 29 have been chosen as primary studies. Our comprehensive analysis will guide practitioners to identify proper access control mechanisms applicable to ensuring security goals in cloud-native architectures.