Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used (...) in CDSC is Linguistic Steganography. Blowfish algorithm is used in CSP Data Security Contrivance to convert the cipher text using a secret-key block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future. (shrink)

Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used (...) in CDSC is Linguistic Steganography. Blowfish algorithm is used in CSP Data Security Contrivance to convert the cipher text using a secret-key block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree (...) algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental (...) results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources (...) and software managements but it possesses a new security risk towards correctness of data stored at cloud. The data storage in the cloud has been a promising issue in these days. This is due to the fact that the users are storing their valuable data and information in the cloud. The users should trust the cloud service providers to provide security for their data. Cloud storage services avoid the cost storage services avoids the cost expensive on software, personnel maintains and provides better performance less storage cost and scalability, cloud services through internet which increase their exposure to storage security vulnerabilities however security is one of the major drawbacks that preventing large organizations to enter into cloud computing environment. This work surveyed on several storage techniques and this advantage and its drawbacks. (shrink)

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond (...) delay, and inefficient use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword (...) searches over encrypted data. This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then (...) dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed (...) data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud (...) class='Hi'>storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements a. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency (...) and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces (...) a secure cloud framework designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes (...) of data, resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long (...) Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long (...) Short- Term Memory (LSTM) and Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data (...) integrity. The paper identifies key challenges associated with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data (...) integrity. The paper identifies key challenges associated with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency (...) and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas (...) industry are focused on first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

As space exploration advances, the need for innovative technologies to handle the ever-growing data and facilitate remote operations beyond Earth becomes critical. Cloud computing is emerging as a transformative force in space missions, enabling data-intensive research, remote collaboration, and the management of large datasets from space missions. This paper explores the role of cloud computing in space exploration, focusing on its potential to support the growing complexity of space missions, improve data storage and processing, (...) and enable real-time remote operations for both astronauts and ground control teams. The paper discusses the challenges of operating in space environments, including latency, bandwidth limitations, and the need for secure data storage. We explore the integration of cloud platforms with space-based technologies such as satellites, rovers, and space stations, and analyze the future of cloud computing in advancing space exploration and interplanetary missions. Finally, we propose best practices for leveraging cloud technologies to enhance space missions and space-based research, improving collaboration, innovation, and operational efficiency. (shrink)

Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has (...) been increasing, presenting substantial dangers to the data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword (...) searches over encrypted data. (shrink)

In cloud secure personal data sharing is the important issues because it creates several securities and data confidentiality problem while accessing the cloud services. Many challenges present in personal data sharing such as data privacy protection, flexible data sharing, efficient authority delegation, computation efficiency optimization, are remaining toward achieving practical fine-grained access control in the Personal Health Information Sharing system. Personal health records must be encrypted to protect privacy before outsourcing to the (...) class='Hi'>cloud. Aiming at solving the above challenges, here propose an efficient data sharing mechanism for Personal Data Sharing, which not only achieves data privacy, fine-grained access control and authority delegation simultaneously, but also optimizes the computation efficiency and is suitable for resource constrained servers. Most of the data consumers are honest, while few of them are corrupt and will leakage their secret keys in the collusion. On the contrary, PKG and data owner are assumed to be fully trusted. Besides, public cloud 1 and public cloud 2 cannot collude with each other. The non-collusive assumption is reasonable, because the client can demand that two cloud servers cannot reveal users’ information by contract. In proposed work, PR-ABE (Attribute Based Encryption with Proxy Re-encryption) technique implements to provide secure encryption of medical data. To improve the access control, here partial key sharing scheme will be implement. Using this, data owner can send partial secret key for the requested user. This approach overcomes the key guessing attack in data retrieval process. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

It is important to provide security and efficient data exchange in cloud infrastructure and achieve traceability and anonymity of data. mean For high levels of safety and performance in one Anonymously, this article addresses the topic It allows data to be exchanged and stored between members of the same group in the cloud. Proposed arrangement creates unique and traceable group data sharing policies using group signatures and special agreements Strategies to accomplish these (...) goals. this Facilitates anonymous communication between systems Public clouds have many users and. Real people following up when needed. Also, the system implements the main agreement programs to make it easier for team members to. Obtain a shared session key for secure data exchange and storage facilities. Basic generation processes a Symmetric Balanced Incomplete Block Theory (SBIBD), significantly reducing the workload of team members a shared session key must be introduced. In cloud computing contexts, the suggested system guarantees efficiency and security for group data sharing, as shown by theoretical analysis and experimental validation. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then (...) dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

Cloud computing has provided many opportunities to businesses and individuals. It enables global and on demand network access to a shared pool of resources with minimal management effort. However, this bliss has become a problem for latency-sensitive applications. To improve efficiency of cloud and to reduce the amount of data that needs to be transported to the cloud for data processing, analysis and storage, a new network architect technology 'Fog Computing' has been introduced. In (...) fog computing, small applications and resources are processed at the edge of the cloud, rather than processing entire applications on the cloud. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, (...) metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of (...) deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of (...) deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid (...) class='Hi'>cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, (...) metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as (...) accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Data access control is a challenging issue in public cloud storage systems. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained and secure data access control for cloud storage with honest-but- curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence it results in a single-point performance bottleneck when a CP-ABE scheme (...) is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low-efficiency of the system. Although multi authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper we propose a system that improves the approach of CP-ABE from text based asymmetric to Image based symmetric approach for faster encryption as well as access to data. We also propose a multiple access policy generation for single user where we will be able to implement one to many and many to many methodology. (shrink)

Data shared in the cloud allows many numerous people exchange their data in a group that helps improve environmental work efficiency. It is a challenging task today how we can assure data security in a single group and also how to share the data with multiple group members, and for it we use a key agreement protocol to share our data in a safe group. In this article we demonstrate a new conceptual agreement (...) protocol, which contributes to the participation of many users who assist may expand many participants in the cloud environment based on the building of the block design, by using the symmetrical block design. Based on the group data sharing of the model. For several participants, we provide a regular approach to retrieve some shared conference key. A key agreement protocol is established, which includes the key to the Conference, in order to ensure the protection of your communication and to ensure that information exchange is secured. Cluster securing plays an essential part in the essential agreement. (shrink)

Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may (...) boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. (shrink)

The convergence of cloud computing, blockchain technology, and the emerging era of quantum computing presents significant challenges for data security. This research tackles these growing vulnerabilities by introducing a comprehensive security framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) to protect data in cloud-based blockchain systems. The primary goal is to safeguard information against quantum threats through QKD, a quantum-secure cryptographic protocol. To enhance resilience against quantum attacks, the framework employs (...) CRYSTALSKyber, a lattice-based encryption mechanism. Additionally, ZKPs are utilized to strengthen privacy and verification processes within cloud and blockchain ecosystems. A key aspect of this study is the performance evaluation of the proposed framework, focusing on encryption and decryption efficiency, quantum key generation rates, and overall system performance. The analysis examines practical considerations such as file size, response time, and computational overhead to assess the framework’s real-world applicability. The findings highlight the framework’s effectiveness in mitigating quantum threats and securing cloud-based blockchain storage. By addressing critical gaps in both theoretical research and practical implementation, this study provides valuable insights for organizations seeking quantum-resistant data security solutions. The framework’s efficiency and scalability demonstrate its feasibility, offering a roadmap for securing cloud environments against the evolving challenges posed by quantum computing and blockchain integration. (shrink)

The exponential growth of cloud computing has revolutionized the IT industry by providing scalable, flexible, and cost-efficient infrastructure solutions. However, as cloud systems become more complex, managing and optimizing these environments becomes increasingly challenging. Traditional cloud management methods often require manual intervention and significant resources to maintain performance, cost-efficiency, and security. Autonomous cloud operations, powered by artificial intelligence (AI) and machine learning (ML), represent the next frontier in cloud management. By leveraging advanced algorithms and (...) real-time data analysis, self-optimizing cloud systems can automate routine tasks such as resource allocation, performance tuning, security management, and fault detection. This paper explores the concept of autonomous cloud operations, highlighting the role of AI and ML in enabling intelligent cloud systems that can autonomously adapt to changing workloads, optimize resource utilization, and improve overall system performance. It also discusses the benefits, challenges, and future prospects of autonomous cloud operations, providing insights into how these technologies can transform cloud management and operations. (shrink)

The convergence of cloud computing, blockchain technology, and the emerging era of quantum computing presents significant challenges for data security. This research tackles these growing vulnerabilities by introducing a comprehensive security framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) to protect data in cloud-based blockchain systems. The primary goal is to safeguard information against quantum threats through QKD, a quantum-secure cryptographic protocol. To enhance resilience against quantum attacks, the framework employs (...) CRYSTALSKyber, a lattice-based encryption mechanism. Additionally, ZKPs are utilized to strengthen privacy and verification processes within cloud and blockchain ecosystems. A key aspect of this study is the performance evaluation of the proposed framework, focusing on encryption and decryption efficiency, quantum key generation rates, and overall system performance. The analysis examines practical considerations such as file size, response time, and computational overhead to assess the framework’s real-world applicability. The findings highlight the framework’s effectiveness in mitigating quantum threats and securing cloud-based blockchain storage. By addressing critical gaps in both theoretical research and practical implementation, this study provides valuable insights for organizations seeking quantum-resistant data security solutions. The framework’s efficiency and scalability demonstrate its feasibility, offering a roadmap for securing cloud environments against the evolving challenges posed by quantum computing and blockchain integration. (shrink)

The Metaverse is poised to transform the digital landscape by providing immersive, interactive, and persistent 3D virtual worlds. For the Metaverse to achieve its full potential, it requires robust, scalable, and low-latency cloud infrastructure to deliver seamless and immersive experiences to users. This paper explores the design considerations for cloud infrastructure tailored to the Metaverse, including scalability, low-latency networking, and high-performance computing. We examine key challenges such as real-time rendering, massive concurrent user support, data storage, and (...) security. Furthermore, we analyze the use of edge computing, content delivery networks (CDNs), and specialized cloud services to address these challenges. Through case studies of current metaverse platforms and cloud providers, we highlight emerging solutions and technologies that can support the Metaverse’s growing demands. Finally, the paper presents future directions in cloud architecture that will facilitate the widespread adoption of the Metaverse, while ensuring an immersive, scalable, and secure environment for users. (shrink)

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the (...) hybrid cloud environment. These strategies aim to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities. (shrink)

Cloud computing has revolutionized data management for businesses and individuals a like, ushering in an era of unprecedented accessibility and scalability. As demand for cloud services continues to surge, the imperative for efficient and secure systems becomes paramount. One approach to meeting this challenge is the consolidation of virtual machines onto fewer physical servers, optimizing resource utilization and yielding significant energy savings. Moreover, this consolidation strategy bolsters overall security by enabling more effective monitoring and control of (...) virtual machine instances. Through the implementation of encryption, access control mechanisms, and intrusion detection systems, sensitive data can be safeguarded against unauthorized access, thereby mitigating potential risks and vulnerabilities inherent in cloud computing environments. In the realm of cloud computing, security and efficiency are paramount concerns, driving innovation and evolution in system design and management. By consolidating virtual machines onto fewer physical servers, organizations can realize tangible benefits in terms of resource optimization and energy efficiency. Yet, beyond the operational advantages, this consolidation approach also enhances the security posture of cloud environments. Through the adoption of robust security measures such as encryption and intrusion detection, sensitive data can be shielded from unauthorized access, bolstering the integrity and trustworthiness of cloudbased systems. (shrink)

: High-performance computing (HPC) plays a crucial role in advancing scientific research and technological innovation by enabling complex simulations, data analysis, and modeling. Azure, Microsoft's cloud computing platform, offers a robust environment for HPC, providing scalable compute power, storage, and advanced tools to accelerate research in fields such as bioinformatics, climate modeling, quantum physics, and engineering. This paper explores how Azure can be optimized for HPC, focusing on the capabilities of Azure’s infrastructure, networking, and services tailored for (...) research and scientific applications. Additionally, it discusses the challenges of utilizing Azure for HPC, such as cost management, resource allocation, and security, while offering practical solutions for maximizing performance. The paper also provides real-world case studies demonstrating the successful application of Azure in HPC-driven research. By leveraging Azure’s HPC resources, researchers and institutions can achieve groundbreaking results while minimizing the barriers of traditional on-premise computing infrastructure. (shrink)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into (...) class='Hi'>data integration techniques, performance optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

The rise of remote work and the widespread adoption of cloud computing have transformed the digital landscape, providing organizations with flexibility and scalability. However, these advancements have also introduced new cybersecurity challenges. Traditional security frameworks, which focus on perimeter defenses, are increasingly inadequate in securing cloud-based environments and remote workforces. This paper explores the evolving landscape of cybersecurity risk management in the context of remote work and cloud computing. It investigates the unique risks associated with these (...) technologies, such as data breaches, misconfigurations, and insider threats, and provides a comprehensive analysis of risk management strategies to address these challenges. Furthermore, the paper examines best practices for securing cloud environments and managing risks associated with remote work, focusing on continuous monitoring, data protection, and employee training. (shrink)

Microsoft Fabric represents a significant leap forward in analytics services, combining data management, analytics, and machine learning into a unified platform. This paper offers a detailed examination of Microsoft Fabric, covering its architecture, prominent features, advantages, and potential applications. Engineered to streamline data processes and foster collaboration among data experts, Microsoft Fabric supports real-time analytics, scalable data storage, advanced machine learning capabilities, and robust security protocols. Future developments for the platform include deeper AI integration, (...) expanded data connectivity, enhanced user experience, fortified security measures, sustainability initiatives, and collaborative tools. By harnessing Microsoft Fabric, organizations can gain comprehensive data insights and drive innovation effectively. (shrink)

The advent of cloud computing has fundamentally transformed various business operations, with Human Capital Management (HCM) standing out as a significant beneficiary. Cloud-based HCM solutions offer unparalleled scalability, accessibility, and integration capabilities, enabling organizations to manage their workforce more efficiently and strategically. This paper explores the revolutionary impact of cloud computing on HCM, highlighting key advancements such as enhanced data analytics, improved employee engagement, cost-effectiveness, and support for remote and hybrid work models. Through a comprehensive literature (...) review and analysis of case studies from diverse industries, the study demonstrates how cloud computing facilitates real-time data access, seamless system integration, and the implementation of advanced technologies like artificial intelligence (AI) and machine learning (ML) within HCM frameworks. Additionally, the research addresses challenges associated with cloud-based HCM adoption, including data security, privacy concerns, and change management. Future research directions are proposed to further leverage cloud computing capabilities, emphasizing the integration of emerging technologies and the continuous evolution of cloud-based HCM tools. The findings underscore the pivotal role of cloud computing in enhancing organizational agility, optimizing human capital, and driving sustainable business growth. (shrink)

Cloud computing has transformed the nature of computation, sharing of information resources, and storage capabilities, including the flexibility to scale these resources for corporate use. Nevertheless, maintaining high reliability in cloud environments is still an issue that has not been solved because of factors such as Hardware failures, network interruptions/slowdowns and software vulnerabilities. This paper discusses several methods that can be employed in the reliability engineering of cloud computing, including fault tolerance, redundancy, monitoring and predictive maintenance. (...) It also further extends the basic reliability measures such as Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Service Availability and Failure Rate, which measure system reliability and effectiveness. Moreover, the paper considers performance assessment methodologies through real-time monitoring, machine learning, and reliability assessment methods. It also addresses the nature and advancement of technologies of artificial intelligence-powered automation and self-healing applications for improved cloud dependability. The present work aims to identify the state-of-the-art state of dependability in cloud services and propose some recommendations for minimizing such costs, improving dependability levels, and reducing undesired downtime. The information is valuable for CSPs, IT designers/architects, and system engineers who wish to create fault-tolerant and optimal cloud environments. (shrink)