Evaluation of Cloud-Based Cyber Security System

Cloud-based cyber security systems leverage the power of cloud computing to protect digital assets from cyber threats. By utilizing remote servers and advanced algorithms, these systems provide real-time monitoring, threat detection, and incident response. They offer scalable solutions, enabling businesses to adapt to evolving threats and handle increasing data volumes. Cloud-based security systems provide benefits such as reduced infrastructure costs, continuous updates and patches, centralized management, and global threat intelligence. They protect against various attacks, including malware, phishing, DDoS, and unauthorized access. With their flexibility, reliability, and ease of deployment, cloud-based cyber security systems are becoming essential for organizations seeking robust protection in today's interconnected digital landscape. The research significance of cloud-based cyber security systems lies in their ability to address the growing complexity and scale of cyber threats in today's digital landscape. By leveraging cloud computing, these systems offer several key advantages for researchers and organizations: Scalability: Cloud-based systems can scale resources on-demand, allowing researchers to handle large volumes of data and analyze complex threat patterns effectively. Cost-efficiency: The cloud eliminates the need for extensive on-premises infrastructure, reducing costs associated with hardware, maintenance, and upgrades. Researchers can allocate resources based on their needs, optimizing cost-effectiveness. Real-time monitoring and threat detection: Cloud-based systems provide real-time monitoring of network traffic, enabling quick identification of suspicious activities and potential threats. Researchers can leverage advanced analytics and machine learning algorithms to enhance threat detection capabilities. Collaboration and knowledge sharing: Cloud platforms facilitate collaboration among researchers and organizations by enabling the sharing of threat intelligence, best practices, and research findings. Compliance and regulatory requirements: Cloud platforms often offer built-in compliance features and tools to meet regulatory requirements, assisting researchers in adhering to data protection and privacy standards. Overall, the research significance of cloud-based cyber security systems lies in their ability to provide scalable, cost-effective, and advanced security capabilities, empowering researchers to mitigate evolving cyber threats and protect sensitive data and systems effectively. We will be using Weighted Product Methodology (WPM) which is a decision-making technique that assigns weights to various criteria and ranks alternatives based on their weighted scores. It involves multiplying the ratings of each criterion by their corresponding weights and summing them up to determine the overall score. This method helps prioritize options and make informed decisions in complex situations. Taken of Operational, Technological, Organizational Recorded Electronic Delivery, Recorded Electronic Deliver, Blockchain technology, Database security, Software updates, Antivirus and antimalware The Organizational cyber security measures comes in last place, while Technological cyber security measures is ranked top and Operational measures comes in between the above two in second place. In conclusion, a cloud-based cyber security system revolutionizes the way organizations safeguard their digital assets. By utilizing remote servers, advanced algorithms, and real-time monitoring, it offers scalable and robust protection against evolving threats. With features like threat detection, data encryption, and centralized management, it ensures enhanced security, agility, and efficiency. Embracing a cloud-based approach empowers organizations to stay ahead in the ever-changing landscape of cyber security, effectively safeguarding their critical data and infrastructure

Industry 5.0 Enabled Smart Logistics: Optimization of Distribution Network in Food Industry

The fourth industrial revolution, namely Industry 4.0, has substantially impacted the supply chain and logistics operations which led to the introduction of Logistics 4.0. The incorporation of novel technologies in this context developed smart logistics; however, scholars raised the concerns about socio-economic aspects of these improvements. Industry 5.0 as a value-driven paradigm, in this regard, initiated the trinary concept of sustainability, resilience, and human-centricity to put forward the technological and conceptual developments of industry according to this framework. Given the recency of this industrial revolution, not many research works have focused on the implication of Industry 5.0 for smart logistics. Therefore, this research aims at bridging this gap by investing effort into accomplishing a thorough systematic literature review to compare the topic of smart logistics in Industry 4.0 and Industry 5.0. The results define integration and intelligence among the key features, and spot simulation and digital twin among the enabling technologies of this concept. To realize these findings, a digital model of a company’s distribution network is created, and it facilitates the possibility of performing network optimization and simulation through an integrated platform. The results show that such approach has a remarkable contribution in performing the supply chain network optimization and determining the logistics performances of the redesigned network, e.g., optimal inventory level and capacity at each facility, shipping policy in individual transportation routes, etc. This approach enables the possibility of incorporating socio-economic aspects into logistics studies, e.g., CO2 emission, which are discussed as further research directions

Towards a unified methodology for supporting the integration of data sources for use in web applications

Organisations are making increasing use of web applications and web-based systems as an integral part of providing services. Examples include personalised dynamic user content on a website, social media plug-ins or web-based mapping tools. For these types of applications to have maximum use for the user where the applications are fully functional, they require the integration of data from multiple sources. The focus of this thesis is in improving this integration process with a focus on web applications with multiple sources of data. Integration of data from multiple sources is problematic for many reasons. Current integration methods tend to be domain specific and application specific. They are often complex, have compatibility issues with different technologies, lack maturity, are difficult to re-use, and do not accommodate new and emerging models and integration technologies. Technologies to achieve integration, such as brokers and translators do exist, but they cannot be used as a generic solution for developing web-applications achieving the integration outcomes required for successful web application development due to their domain specificity. It is because of these difficulties with integration, and the wide variety of integration approaches that there is a need to provide assistance to the developer in selecting the integration approach most appropriate to their needs. This thesis proposes GIWeb, a unified top-down data integration methodology instantiated with a framework that will aid developers in their integration process. It will act as a conceptual structure to support the chosen technical approach. The framework will assist in the integration of data sources to support web application builders. The thesis presents the rationale for the need for the framework based on an examination of the range of applications, associated data sources and the range of potential solutions. The framework is evaluated using four case studies

Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Improving efficiency and reducing waste for sustainable beef supply chain

In this thesis, novel methodologies were developed to improve the sustainability of beef supply chain by reducing their environmental and physical waste. These methodologies would assist stakeholders of beef supply chain viz. farmers, abattoir, processor, logistics and retailer in identification of the root causes of waste and hotspots of greenhouse emissions and their consequent mitigation. Numerous quantitative and qualitative research methods were used to develop these methodologies such as current reality tree method, big data analytics, interpretive structural modelling, toposis and cloud computing technology. Real data set from social media and interviews of stakeholders of Indian beef supply chain were used. Numerous issues associated with waste minimisation and reducing carbon footprint of beef supply chain are addressed including: (a) Identification of root causes of waste generated in the beef supply chain using Current Reality Tree method and their consequent mitigation (b) Application of social media data for waste minimisation in beef supply chain. (c) Developing consumer centric beef supply chain by amalgamation of big data technique and interpretive structural modeling (c) Reducing carbon footprint of beef supply chain using Information and Communication Technology (ICT) (d) Developing cloud computing framework for sustainable supplier selection in beef supply chain (e) Updating the existing literature on improving sustainability of beef supply chain. The efficacy of the proposed methodologies was demonstrated using case studies. These frameworks may play a crucial role to assist the decision makers of all stakeholders of beef supply chain in waste minimization and reducing carbon footprint thereby improving the sustainability of beef supply chain. The proposed methodologies are generic in nature and can be applied to other domains of red meat industry or to any other food supply chain

Innovation Landscape in developed and developing markets. A conceptual and empirical study on technology convergence and low cost innovations

Innovation is proven to be an absolute requirement for growth in both developed and developing countries, but the type and motivation of innovations differ depending on various surrounding factors like socio-cultural attributes, geography, infrastructure, political environment and income-levels of customers. In developed countries innovations are often technology-driven and associated with delighting the end customers. On the contrary, in the developing or emerging markets due to the unique setting and infrastructural gaps innovations are focused towards meeting customer’s fundamental needs. Innovations in emerging markets are also seen as one of the drivers, utilized to address urgent developmental challenges such as poverty, illiteracy and lack of access to healthcare services. Considering these vast differences in driving factors, this research focuses on the comparison of the ongoing innovation fostering in both developed and developing world individually. This thesis is an attempt to understand the innovation landscape across these two worlds and focus on specific innovation approaches based on their potential and relevance. In developed world, information technology (IT) is emerging out as the key enabling technology across different innovation approaches. This thesis focuses on one such innovative application of IT called technology convergence, which is an integration of information and operational technologies (Gartner, 2011). Significant financial and productivity benefits are expected from this convergence and therefore many industrial companies are investing heavily into this alignment and undergoing huge business transformations. This study analyses the case of General Electric undergoing such a strategic business transformation. Study conceptualizes a theoretical framework around this new concept by expanding the Venkatraman’s (1994) IT-enablement model and exhibiting evidences of non–linearity and overlap across different transformation stages. Study discloses, IT localized exploitation stage as a default stage for initiating technology convergence and illustrates that each stage of the transformation has an impact on a unique set of organizational dimensions. Business scope redefinition stage influences the dimension of strategy and vision while internal integration stage influences the organization’s structure dimension. The two dimensions that are impacted most during the business process and network redesign stages, are business process and products and markets respectively. In contrast to the developed world where innovation approaches are focused on IT enabled performance enhancements, emerging markets are observing innovations centred on frugal products that are cost effective and provide value for money. Past two decades have seen a tremendous growth in emerging markets as they are developing their own innovative capabilities (Jiatao and Rajiv, 2009). Country like India, which is also a focus of this thesis, initially playing secondary roles has now become a breeding ground for frugal and social global innovations. This thesis discusses various types of innovation approaches adopted by local firms and multi-national companies in emerging markets such as frugal innovation, jugaad, disruptive innovation, gandhian innovation, catalytic innovation, indigenous innovation, resource-constrained innovation and bottom-up innovation. It identifies the increasing complexity and terminology confusion across these approaches in the growing and fragmented literature revolving around emerging markets. Targeting this shortcoming of the literature, this study attempts to consolidate the research insights into a unified framework defining eight main requirements of emerging markets namely cost-effective, easy-to-use, sustainable, problem-centric, no-frills, fast-to-market, resourceful and breakthrough. Additionally, study also analyses the priorities of these requirements during the buying and designing process from end customers and manufacturers point of view respectively. Research confirms “cost-effective” and “easy to use” as the absolute requirements of bottom-of-pyramid (BOP) customers and reveals the growing awareness towards eco-friendly products. It also introduces two additional important features from customer perspective namely – low/no maintenance or consumables and customized solutions to the framework. Furthermore, research also touches upon the topic of social enterprises, medium to diffuse social innovations into emerging markets to address social challenges and developmental issues like poverty and access to healthcare services. Study uses event structure analysis and four growth stages identified by Perrini et al. (2010); opportunity identification, opportunity evaluation, opportunity exploitation and opportunity scaling-up to analyze two social healthcare enterprises in India. It proposes an abstract model of a social enterprise with the contributing generalized actions and their causal interactions. Thesis ends with a conclusion giving an overview and a consolidated view of innovation approaches existing in developed world and emerging markets. Additionally it reemphasises some of the limitations experienced during the research work and suggests related future research propositions

Digital transformation in the manufacturing industry : business models and smart service systems

The digital transformation enables innovative business models and smart services, i.e. individual services that are based on data analyses in real-time as well as information and communications technology. Smart services are not only a theoretical construct but are also highly relevant in practice. Nine research questions are answered, all related to aspects of smart services and corresponding business models. The dissertation proceeds from a general overview, over the topic of installed base management as precondition for many smart services in the manufacturing industry, towards exemplary applications in form of predictive maintenance activities. A comprehensive overview is provided about smart service research and research gaps are presented that are not yet closed. It is shown how a business model can be developed in practice. A closer look is taken on installed base management. Installed base data combined with condition monitoring data leads to digital twins, i.e. dynamic models of machines including all components, their current conditions, applications and interaction with the environment. Design principles for an information architecture for installed base management and its application within a use case in the manufacturing industry indicate how digital twins can be structured. In this context, predictive maintenance services are taken for the purpose of concretization. It is looked at state oriented maintenance planning and optimized spare parts inventory as exemplary approaches for smart services that contribute to high machine availability. Taxonomy of predictive maintenance business models shows their diversity. It is viewed on the named topics both from theoretical and practical viewpoints, focusing on the manufacturing industry. Established research methods are used to ensure academic rigor. Practical problems are considered to guarantee practical relevance. A research project as background and the resulting collaboration with different experts from several companies also contribute to that. The dissertation provides a comprehensive overview of smart service topics and innovative business models for the manufacturing industry, enabled by the digital transformation. It contributes to a better understanding of smart services in theory and practice and emphasizes the importance of innovative business models in the manufacturing industry