Cloud Data Security Research Articles

Dynamic security in cloud computing based on fuzzy cognitive maps

Cloud Computing is a new paradigm that provides software and hardware resources based on customers’ needs. However, data security remains a significant concern, acting as a major barrier to its widespread adoption. The objective of this work is to propose a process for assessing data risks in Cloud Computing. The process is based on fuzzy cognitive maps (FCMs) for qualitative reasoning. Risk assessment is a crucial aspect of Cloud Computing. Our proposed system involves three main steps based on expert knowledge. In the first step, a global FCM was built and validated to identify threats at the Cloud Computing level. Then the second step was devoted to calculate the consequences by inference using FCM Expert. The obtained results show that the FCM has a remarkable capacity to deal with the uncertainty of the data; by doing so, it becomes possible to model the real system more accurately. The last step is to simulate, analyze the results of the second step in Matlab Simulink and also develop recommendations to reduce the risks in order to improve and increase the security of Cloud data.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Revolutionizing Cloud Security: A Novel Framework for Enhanced Data Protection in Transmission and Migration

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability—core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system’s capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies.

Sap S/4hana applications on data security and protections for sap cloud services

This study focuses on how to secure enterprise resource planning (ERP) systems that are hosted in the cloud. We utilized SAP S/4HANA cloud (Public edition) for this research. Here we take a look at the difficulties and concerns around cloud data security. The essay will provide a comprehensive analysis of the research findings. We implement global protection strategies and policies to mitigate risks and threats, guaranteeing the highest level of data safety in Cloud ERP. While many apps would benefit from access to ERP data in the cloud, doing so would also make that data available to apps that may already have security flaws.We chose to investigate cloud-based ERP since many organizations are moving away from on-premises ERP systems in favor of them. Concerns regarding the safety of data while it is in transit or at rest are also covered in the article. The study makes use of platforms as a service (PaaS) and software as a service (SaaS) at all levels. As part of our investigation, we look at potential fixes for several Cloud ERP security vulnerabilities.

Enhancing Cloud Data Security using Artificial Neural Networks for Users’ Account Hijacking Security Threats

Objectives: To ensure the security of passwords of cloud users' accounts that cannot be decrypted easily by any software or hackers. Methods: In this manuscript, we have designed an experimental setup using a feed-forward back-propagation algorithm of Artificial Neural Networks techniques to ensure cloud data security. For this purpose, we have utilized password-based datasets created by us. 70% of the datasets are allocated for training and 30% for testing and validation purposes. In this training, TRAINLM training function, LEARNGDM adaptive function, performance function is MSE, and PURELIN and TANSIG transfer function to transfer the neurons from input to hidden layer and hidden to the output layer has been used. The minimum learning rate is set to 0.001. Findings: A username and password are required to access cloud services. A cloud-based storage system is used to store login credentials. Due to inadequate security, attackers use hacking techniques to gain access to users’ accounts. Attackers steal data, resources, or services from these accounts. To ensure the security of passwords of cloud users' accounts, we have designed an experimental setup using a feed-forward back-propagation algorithm of neural network techniques. Through this approach, the passwords are stored in a weight matrix, which is a multi-dimensional structure. Novelty: The dimensions of the weight matrix are obscured, making it impossible for the hackers to determine its structure. As a result, we conclude that cloud data is effectively secured on the cloud server. Keywords: Artificial Neural Networks, Cloud Computing, Data Security, Feed-Forward Back-Propagation Algorithm, Machine Learning

Certificateless Proxy Re-encryption with Cryptographic Reverse Firewalls for Secure Cloud Data Sharing

Cloud computing has enabled data-sharing to be more convenient than ever before. However, data security is a major concern that prevents cloud computing from being widely adopted. A potential solution to secure data-sharing in cloud computing is proxy re-encryption (PRE), which allows a proxy to transform encrypted data from one key to another without accessing the plaintext. When using PRE, various challenges arise, including the leak of information by a trusted third party, collusion attacks, and issues associated with revocation. To overcome these challenges, this paper proposes a novel Certificateless Proxy Reencryption with Cryptographic Reverse Firewall for Secure Cloud Data Sharing (CLPRE-CRF). The new scheme enables secure distribution of encrypted data from a data owner to users through public clouds. Meanwhile, the CLPRE-CRF scheme can resist exfiltration of secret information and forgery of ciphertext in case the scheme is compromised. In addition, the scheme provides a flexible revocation mechanism to prevent unauthorized access to private data. The security analysis demonstrates that the CLPRE-CRF resists chosen-plaintext attacks and collusion attacks. Moreover, performance evaluation indicates that our scheme achieves a 14% and 22% reduction in computation costs during the encryption and decryption algorithms, respectively. Therefore, the proposed CLPRE-CRF scheme is well-suited for cloud computing environments.

Expediting the design and development of secure cloud-based mobile apps

The adoption and popularity of mobile devices by end-users is partially driven by the increasing development and availability of mobile applications that can aid solving different problems and provide access to services in a wide range of domains or categories, namely healthcare, education, e-commerce or entertainment. While these applications use and benefit from the combination of a wide panoply of technologies from the Internet of Things, fog and cloud computing, data security and privacy are typically not fully taken into account before the creation of many mobile applications or during the software development phases. This paper presents an in-depth approach to modeling attacks on the specific cloud and mobile ecosystem, given its importance in the process of secure application development. Moreover, aiming at bridging the knowledge gap between developers and security experts, this paper presents an alpha version of the security by design for cloud and mobile ecosystem (secD4CloudMobile) framework. secD4CloudMobile is a set of tools that covers cloud and mobile security requirement elicitation (CMSRE), cloud and mobile security best practices guidelines (CMSBPG), cloud mobile attack modeling elicitation (CMAME), and cloud mobile security test specification and tools (CM2ST). The purpose of the framework is to provide cloud and mobile application developers useful readily applicable information and guidelines, striving to bring security engineering and software engineering closer, in a more accessible and automated manner, aiming at the incorporation of security by construction. Finally, the paper presents some preliminary results and discussion.

Quantum-Inspired Cryptography Protocols for Enhancing Security in Cloud Computing Infrastructures

As we know, cloud computing plays a vital role in our lives. It also plays a pivotal role in modern business and information technology landscapes. But with this, there is a rise in concerns regarding the security and privacy of all data which is stored in the cloud. To tackle these concerns some effective and traditional cryptographic methods are present but they face potential vulnerabilities due to advent of quantum computing. It also threatens the basis of widely used encryption algorithms and models. This research focuses on the usage of quantum mechanics inspired cryptographic protocols, which will help to strengthen the security of cloud computing models and infrastructures. The research begins with a detailed overview of the current situation and state of security of cloud computing. There is identification of all challenges and vulnerabilities faced due to current, classical and traditional cryptographic techniques and algorithms. As we know that there is a very close arrival of quantum computing, we are surrounded by mighty and latent threats because of quantum computing algorithms that can easily overcome the widely accepted and used encryption standards and models. So, there is a dire need to develop and implement some alternative cryptographic strategies and measures. To overcome these challenges, this research proposes the creation and usage of quantum-inspired cryptographic protocols which are designed to resist attacks from both traditional and quantum vulnerabilities. These protocols are inspired from quantum mechanics, like superposition and entanglement. These protocols will help to design and create cryptographic systems and models that offer enhanced security. The research focuses on the theoretical foundations of these quantum mechanics inspired protocols and assesses their practical feasibility within cloud computing infrastructures and environment.This research contains information regarding the challenges associated with integration and deployment of quantum inspired cryptographic solutions in cloud computing. It evaluates the scalability, efficiency, performance and computational overhead of these quantum inspired protocols to ensure their practical feasibility and capability in existing cloud computing environment and infrastructures. This research also includes comparison performed with quantum cryptographic protocols and traditional cryptographic methods. The results aim to provide evidence that quantum cryptographic techniques are better, feasible and secure options to protect data over cloud. In Conclusion, the research aims to provide an insight that development and usage of advanced security measures can withstand and handle evolving threats, ensuring the continued security and protection of data in cloud computing environment by providing theoretical insights, analysis and evolution.

THC-DFECC-based privacy preserved smart contract creation for cloud data security

THC-DFECC-based privacy preserved smart contract creation for cloud data security

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Introduction: The advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Methods: Hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: It is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusion: The findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use.

Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security

ABSTRACT In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).

A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents

Even though cloud computing has advanced over the years, the two major challenges currently faced by cloud computing are data security and trusted computing. In cloud computing applications, blockchain technology can enhance data security and trusted computing when paired with virtual machine agents and mobile agent technologies. A decentralized new framework for distributed computing is blockchain. A potential area of study is the integration of blockchain technology with cloud computing, leveraging the former's security mechanisms to enhance the latter's secure computing and storage capabilities. The usage of the proposed framework builds on the inherent security features of blockchain technology in an effort to secure data and information integrity, confidentiality and authentication in cloud environments. Through the introduction of distributed virtual machine agents, the framework increases the scalability and effectiveness of data protection mechanisms for virtual data environments that can be used to store information protected by cloud security measures. Thus, the proposed study substantiates the applicability and efficiency of the existing and emerging problems of cloud data security and this approach develops satisfactory data protection solutions.

A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents

A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents

Multi-Level Encryption System using AES and RSA Algorithms

Abstract: As the volume of sensitive data entrusted to the cloud explodes, from healthcare records to financial transactions, the need for robust security solutions intensifies. Traditional encryption techniques, while effective, remain vulnerable to key compromise, leaving data exposed to malicious factors. This study proposes a multi-level encryption strategy that leverages the power of AES for efficient data encryption, RSA for secure key protection with public-key cryptography, and role-based encryption for granular access control. This layered approach significantly increases the computational complexity for unauthorized access, making brute-force or dictionary attacks exponentially more difficult. Our research demonstrates successful integration of these algorithms, achieving a 50% reduction in key compromise risk and enhanced data integrity through MAC verification. Moving forward, we envision seamless integration with multiple cloud platforms and exploring advanced access control mechanisms based on user context and behavior. This multi-level encryption strategy holds immense potential to reshape the landscape of cloud data security, fostering a more secure and trustworthy digital future.

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

PRC6: Hybrid lightweight cipher for enhanced cloud data security in parallel environment

AbstractModern technologies of computing cloud are showing great promise, but at the same time create new security challenges that hinder full acceptance. Given that most of these services often use cloud networks as channels for communication, securing data transmission is crucial. This paper introduce a new hybrid encryption algorithm, the proposed two‐layered PRC6 cipher, tailored address security concerns in cloud computing environments with minimal resource constraints. The PRC6 cipher incorporates enhancements from Cha‐cha into an extension of the RC6 cipher. PRC6 implements double encryption. At the first level, the plain text is divided into four equal parts, each encrypted by processes derived from RC6, which include shifting, summation, modulo arithmetic, and XOR with a generated key. The second level incorporates a Quarter round function, among others, to further obscure the encoded message. PRC6 is implemented in a parallel computing model to significantly reduce overall computation time, especially important for lightweight applications. Experimental results show that the algorithm can achieve a high level of security for cloud workloads. It activates parallel mode in just seven encryption rounds, cutting calculation time to 50% in a matter of seconds. Performance evaluations against popular encryption standards also indicate that PRC6 offers promising security benefits when computational resources are limited. This hybrid approach presents a viable solution for strengthening data protection in modern cloud systems and it stand against the most popular attacks like brute force.

Emergence of Novel WEDEx-Kerberotic Cryptographic Framework to Strengthen the Cloud Data Security against Malicious Attacks

Emergence of Novel WEDEx-Kerberotic Cryptographic Framework to Strengthen the Cloud Data Security against Malicious Attacks

Enhancing data security in cloud computing through the implementation of revocable storage and identity-based encryption

Enhancing data security in cloud computing through the implementation of revocable storage and identity-based encryption

Cloud data security for distributed embedded systems using machine learning and cryptography

Cloud data security for distributed embedded systems using machine learning and cryptography

A secure and lightweight cloud data deduplication scheme with efficient access control and key management

Data deduplication technology is extensively employed to enhance the storage efficiency of cloud servers by eliminating redundant files. Cloud users commonly encrypt their data prior to uploading it to the server. Conventional encryption algorithms, however, lead to the encryption of duplicated data from different users into distinct ciphertexts. Consequently, these ciphertexts must be stored in the cloud since the cloud server cannot identify such duplicated data. In this paper, we introduce a hybrid cloud-based secure deduplication scheme tailored for implementation on large-scale data systems. Specifically, our approach leverages ciphertext-policy attribute-based encryption (CP-ABE), which enables us to establish access control and key management via a private cloud server. Simultaneously, we leverage a public cloud server to cater to enterprises and groups seeking secure data storage. Notably, our approach ensures mutual zero-interaction verification between both public and private cloud servers through ElGamal encryption, thereby guaranteeing data unforgeability. The security assessment illustrates that our proposed approach ensures both data privacy and integrity. We also show that the approach resists brute-force attacks on the dictionary, prevents malicious users from deceiving cloud servers to return incorrect ciphertext, and achieves secure and efficient access control and key management. Furthermore, functional and performance evaluation underscores the superiority of our method over five other classical data deduplication schemes. Under the premise of having more comprehensive security settings, the performance of the scheme still maintains a good level at every stage.