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)

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)

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)

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)

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 storage alternative. Exploring sophisticated (...) machine learning algorithms for predictive search improvements a. (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)

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)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

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)

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)

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)

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)

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)

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)

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)

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 (...) class='Hi'>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)

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)

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)

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)

Securing sensitive files stored on cloud servers presents a paramount challenge in today's digital landscape, demanding a robust file auditing mechanism that transcends traditional methods. This paper introduces an innovative approach to file auditing in cloud environments, distinguished by the integration of secure download key notifications with login processes. Unlike conventional file auditing techniques that primarily focus on monitoring file activities, this proposed solution recognizes the critical need for securely tracking file downloads. To address this gap, we (...) present a sophisticated system that not only comprehensively audits file activities but also seamlessly integrates with login processes to provide secure download key notifications. By bridging the gap between file auditing and user authentication, our solution enhances the security posture of cloud-based file storage systems, empowering organizations to safeguard their sensitive data effectively. Through detailed analysis and evaluation, we demonstrate the efficacy and practicality of our approach in enhancing the security and integrity of sensitive files stored on cloud servers. (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)

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. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...) made, like adding wearable device data in real-time or investigating deep learning techniques. (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 (...) class='Hi'>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)

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)

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)

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)

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)

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)

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)

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)

Due to risks like tampering, corruption, and illegal access, the rapid rise in cloud storage usage makes data integrity a top priority. As a way to verify the correctness of data stored in the cloud, we develop a "Data Integrity Verification Scheme" in this project which involves the use of third-party auditing (TPA). The system produces hash values for files both during upload and retrieval using cryptographic hashing methods, assuring consistency between both. Without needing a link to (...) the real file contents, the third-party auditor independently confirms the accuracy of the data that was stored. The Google Cloud SDK and 2 Python-based encryption libraries, hashlib and pycryptodome, are utilized in the project. Our approach is meant to be safe and efficient, providing real-time verification of integrity for data stored in cloud. . (shrink)

This Review paper is about the security of bio metric templates in cloud databases. Biometrics is proved to be the best authentication method. However, the main concern is the security of the biometric template, the process to extract and stored in the database within the same database along with many other. Many techniques and methods have already been proposed to secure templates, but everything comes with its pros and cons, this paper provides a critical overview of these issues (...) and solutions. (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 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)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze (...) network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (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)

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 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)

Cloud computing has transformed how businesses design, deploy, and manage applications. With its ability to provide on-demand resources and scalability, cloud platforms offer significant benefits in terms of flexibility, cost-efficiency, and speed. However, to fully leverage these advantages, it is crucial to apply proven design patterns that ensure the cloud architecture is both scalable and secure. This paper explores key cloud architecture design patterns, including microservices, serverless architecture, multi-cloud, and hybrid cloud, along with (...) best practices for achieving scalable and secure systems. Emphasizing security and scalability, it provides guidance for organizations on how to build resilient and high-performing cloud systems. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment frameworks to prioritize effective (...) countermeasures. Our findings indicate that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud Computing. The (...) key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (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 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)

As cloud-native architectures continue to evolve, microservices have become the foundation for scalable and resilient applications. However, the decentralized nature of microservices introduces significant security challenges, including service-to-service communication security, identity management, and traffic control. Service mesh technologies, such as Istio, Linkerd, and Consul, provide a powerful solution by offering decentralized security enforcement, mutual TLS (mTLS) encryption, fine-grained access control, and observability without modifying application code. This paper explores how service meshes enhance microservices security by implementing zero-trust policies, automatic (...) traffic encryption, and service authentication mechanisms. Through the integration of policy-based access control, workload identity, and anomaly detection, service mesh architectures significantly reduce the risk of unauthorized access, lateral movement attacks, and data breaches. Additionally, this study highlights best practices for deploying secure service meshes in cloud-native environments, ensuring compliance with industry security standards. The findings demonstrate that adopting a service mesh improves security posture while maintaining agility and performance in microservices-based applications. (shrink)

Nowadays Fog Computing has become a vast research area in the domain of cloud computing. Due to its ability of extending the cloud services towards the edge of the network, reduced service latency and improved Quality of Services, which provides better user experience. However, the qualities of Fog Computing emerge new security and protection challenges. The Current security and protection estimations for cloud computing cannot be straightforwardly applied to the fog computing because of its portability and heterogeneity. (...) So these issues in fog computing arises new research challenges and opportunities. This survey features about existing security concerns for fog computing and new proposed system to tackle some of the issues in fog computing related to security and privacy, thereby enhancing the cloud security. (shrink)

In the current era dominated by cloud computing, networking infrastructure serves as the backbone of digital operations. Among the top cloud service providers, Microsoft Azure offers a robust suite of networking solutions tailored to meet the evolving needs of modern businesses. This document aims to provide a comprehensive overview of Azure networking, examining its key components, deployment models, best practices, and practical applications. By exploring Azure Virtual Network, Load Balancer, VPN Gateway, ExpressRoute, Firewall, and other services in detail, (...) the goal is to equip organizations with the necessary knowledge and insights to effectively leverage Azure networking for seamless cloud connectivity and enhanced security. (shrink)

Hybrid cloud environments combine private and public cloud infrastructures to optimize security, scalability, and cost-effectiveness. However, ensuring secure access control in such environments remains a critical challenge due to dynamic workloads, multi-tenancy, and cross-cloud authentication complexities. This paper explores access control models tailored for secure hybrid cloud deployment, focusing on Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and emerging Zero Trust principles. We analyze their effectiveness in mitigating unauthorized access, privilege escalation, and insider (...) threats. Additionally, a novel hybrid model integrating RBAC and ABAC is proposed to enhance security and flexibility while ensuring compliance with regulatory frameworks. The study also highlights the role of federated identity management and blockchain-based access control mechanisms in strengthening authentication and authorization in hybrid cloud environments. The results indicate that adaptive access control strategies can significantly enhance security without compromising performance. Future research should focus on AI-driven access control mechanisms and self-learning security models to further improve dynamic access control in hybrid cloud settings. (shrink)

While cloud computing is picking up prevalence, assorted security and protection issues are rising that block the quick reception of this new computing worldview. Furthermore, the improvement of cautious arrangements is lingering behind. To guarantee a safe and reliable cloud environment it is fundamental to distinguish the impediments of existing arrangements and imagine headings for future research. In this paper, we have reviewed basic security and protection challenges in cloud computing, arranged different existing arrangements, looked at their (...) qualities and constraints, and imagined future research headings. (shrink)

Homomorphic encryption is a transformative cryptographic technique that enables secure cloud data processing by allowing computations on encrypted data without requiring decryption. Unlike traditional encryption methods, which protect data only at rest and in transit, homomorphic encryption ensures end-to-end security, even during computation. This capability is particularly vital for industries that rely on cloud computing while handling sensitive information, such as finance, healthcare, and government sectors. However, despite its strong security guarantees, the widespread adoption of homomorphic encryption (...) remains limited due to its high computational complexity and performance overhead. This paper examines the role of homomorphic encryption in secure cloud data processing by evaluating its advantages, challenges, and real-world applicability. First, a comprehensive literature review contrasts traditional and homomorphic encryption techniques, highlighting their security implications and efficiency trade-offs. Second, experimental simulations are conducted to assess computation time, scalability, and data transfer efficiency in cloud environments. The results underscore that while homomorphic encryption enhances data security, its feasibility depends on specific use-case requirements, balancing security, performance, and cost. Finally, we explore practical applications such as privacy-preserving data analytics and secure outsourcing of computations, demonstrating how homomorphic encryption can redefine cloud security paradigms. Keywords: Homomorphic encryption, cloud security, encrypted computation, privacy-preserving data processing, secure cloud computing. (shrink)

In the era of cloud computing, securing access to cloud instances is paramount for protecting sensitive data and ensuring compliance with industry standards. Secure Shell (SSH) access management plays a critical role in safeguarding cloud environments by controlling how users interact with cloud-based systems. However, traditional SSH access management methods can often be cumbersome, error-prone, and vulnerable to breaches. This paper explores the implementation of Privileged Access Management (PAM) solutions for enhancing the security of SSH (...) access to cloud instances. By integrating PAM systems with cloud infrastructure, organizations can enforce stricter access controls, enable detailed session auditing, and reduce the risk of unauthorized access. The paper discusses best practices for managing SSH keys, monitoring user activity, and automating access management workflows using PAM solutions, ultimately improving the security posture of cloud environments. (shrink)

The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .