Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv

Enhancing Institutional Assessment and Reporting Through Conversational Technologies: Exploring the Potential of AI-Powered Tools and Natural Language Processing

This study explores the potential of conversational technologies, AI-powered tools, and natural language processing (NLP) in enhancing institutional assessment and reporting processes in higher education. The traditional approach to assessment often involves labor-intensive manual analysis of extensive data and documents, which burdens institutions. To address these challenges, AI-powered tools, such as ChatGPT, LangChain, Poe, Claude, and others, along with NLP techniques, are investigated in relationship to their ability to improve institutional assessment practices and output. By leveraging these advanced technologies, assessment officers and institutional effectiveness, researchers can engage in dynamic conversations with data, transforming spreadsheets and documents from static artifacts into interactive resources. These tools streamline communication, collaboration, and decision-making processes, empowering committees and working groups to achieve their goals effectively. Additionally, the potential applications of NLP in analyzing vast amounts of institutional data, including student feedback, faculty evaluations, and institutional documents, shall be discussed. Language models enable the extraction of meaningful insights from unstructured data sources, facilitating real-time decision-making processes. Ethical considerations related to data privacy, mining, and compliance with regulations like FERPA are crucial aspects addressed in this study. The contribution of this research lies in uncovering the transformative impact of conversational technologies, AI-powered tools, and NLP techniques on institutional assessment and reporting. By embracing these advancements responsibly and ensuring alignment with ethical principles, institutions can unlock the full potential of these tools, facilitating more efficient, data-driven decision-making processes in higher education. The study showcases how conversational technologies, AI-powered tools, and NLP techniques offer new possibilities for improving institutional assessment and reporting practices. By integrating these technologies responsibly and addressing ethical considerations, institutions can enhance their assessment processes and make more informed decisions based on comprehensive, real-time insights

Capability maturity model and metrics framework for cyber cloud security

© 2017 SCPE. Cyber space is affecting all areas of our life. Cloud computing is the cutting-edge technology of this cyber space and has established itself as one of the most important resources sharing technologies for future on-demand services and infrastructures that support Internet of Things (IOTs), big data platforms and software-defined systems/services. More than ever, security is vital for cloud environment. There exist several cloud security models and standards dealing with emerging cloud security threats. However, these models are mostly reactive rather than proactive and they do not provide adequate measures to assess the overall security status of a cloud system. Out of existing models, capability maturity models, which have been used by many organizations, offer a realistic approach to address these problems using management by security domains and security assessment on maturity levels. The aim of the paper is twofold: first, it provides a review of capability maturity models and security metrics; second, it proposes a cloud security capability maturity model (CSCMM) that extends existing cyber security models with a security metric framework

Cloud based collaborative software development: A review, gap analysis and future directions

Organizations who have transitioned their development environments to the Cloud have started realizing benefits such as: cost reduction in hardware; relatively accelerated development process via reduction of time and effort to set up development and testing environments; unified management; service and functionality expansion; on-demand provisioning and access to resources and development environments. These benefits represent only a fraction of the full potential that could be achieved via leveraging Cloud Computing for the collaborative software development process. Related efforts in this area have been mainly in the areas of: asynchronous collaboration; collaboration in isolated aspects of the Software Development process, such as coding activities; use of open-source tools for contributing, improving, and managing code, etcetera. Although these efforts represent valid contributions and important enablers, they are still missing important aspects which enable a more holistic process, with solid theoretical foundation. This paper reviews this research area, in order to better assess factors and gaps creating the need to enhance the collaborative software development process in the Cloud, to better meet the pressure to collaboratively create better cloud-agnostic applications. © 2017 IEEE

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments