Cloud Computing Security Research Articles

A Delayed Malware Propagation Model for Cloud Computing Security

A Delayed Malware Propagation Model for Cloud Computing Security

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.

Extreme learning with projection relational algebraic secured data transmission for big cloud data

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

Enhancing Data Security and Accountability in Cloud Computing: A Decentralized Cloud Information Accountability Framework

Enhancing Data Security and Accountability in Cloud Computing: A Decentralized Cloud Information Accountability Framework

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Cloud Computing Security Issues, Challenges, and Solutions

Cloud computing has revolutionized the way agencies manage and deliver their IT offerings by means of imparting scalable, bendy, and fee-effective solutions. However, with these good sized blessings come complicated protection demanding situations. The allotted nature of cloud environments, coupled with multi-tenant infrastructures, offers new risks that require sturdy and adaptive security frameworks. This paper goals to provide an in-intensity exploration of the primary security problems in cloud computing, the underlying challenges companies face in securing cloud services, and the solutions designed to mitigate these dangers

Cloud Computing Security: Assured Deletion

Cloud Computing Security: Assured Deletion

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

Enhanced Cloud Computing Security Based on Single to Multi Cloud Systems

Enhanced Cloud Computing Security Based on Single to Multi Cloud Systems

A Review: Security Mechanisms in Cloud Computing

Abstract: Several administrations, including programming, gathering, and coordinating equipment assets, are included in Cloud Computing (CC) and made available to service users online. The advantages of Cloud Computing are flexibility, competence, and high unwavering quality. Numerous organizations are already exchanging data to the Cloud, and as a result, this data needs to be protected against unauthorized assaults, service rejection, and other threats. Information is deemed secure if classification, accessibility, and uprightness are all available. The challenges and problems related to Cloud Computing security are illustrated in this paper. Additionally, research on security protocols for Cloud-based settings is carried out.

Survey of Cloud Computing Security and Privacy Issues

The world of computing and data management has completely changed as a result of cloud computing, which offers both consumers and enterprises scalable resources, on-demand services, and considerable cost savings. The demand for more adaptable and effective computer resources has prompted this paradigm change, and cloud computing provides it by enabling users to access and utilize computing resources over the internet. But using cloud computing also raises a number of security and privacy issues that must be properly handled. In order to assess the present status of cloud computing security and privacy problems, this article will examine key terms, the development of the technology, its significance, current research gaps, and the urgent need for more in-depth analysis.

Privacy and Security in the Cloud: Re-thinking about the State of Research in Tanzania and South Africa

Abstract Cloud computing is widely embraced worldwide because of its significant economic advantages. Nevertheless, the implementation of this technology exacerbates concerns over privacy and security. This article seeks to examine the main body of research on privacy and security in cloud computing, with a specific emphasis on Tanzania and South Africa. The author claims that the literature is continuously developing. There is a need for more research on the subject from a technological standpoint. The survey indicates a distinct prospect for future investigation and governance. This article suggests that further investigation should be undertaken from a legal perspective.

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.

Enhanced Data Security and Storage Efficiency in Cloud Computing: A Survey of Data Compression and Encryption techniques

Enhanced Data Security and Storage Efficiency in Cloud Computing: A Survey of Data Compression and Encryption techniques

Generative adversarial networks-based security and applications in cloud computing: a survey

Generative adversarial networks-based security and applications in cloud computing: a survey

Review of Data Security and Privacy in Cloud Computing

Cloud computing, thus, ensure more healthy demand for cloud services, certain measures against threats in order to protect information must be taken. In essence, the current paper seeks to extend knowledge regarding the improvement of paradigms of cloud computing with particular focus on the aspect of certification of authentic users and protection of the contents that are hosted in these environments. It mentions a new approach in the context of the authentication method that was deployed for the purpose of the AAA certification is presented there along with the watermarking and this RSA algorithms to enhance the security of the cloud file-sharing. This concept is far more effective than the conventional access policies that are top down and cal beforehand for systematic risk avoidance; hence, it helps to minimize the chances of an organization being vulnerable to various risks by right management. Iturbide’s data loss prevention technique eliminates the probability of losing sensitive information to the rest of the world as well as other unauthorized persons or organizations regaining access to privileged keys while Shamir’s secret sharing algorithm- polynomial interpolation ensures that the generation of keys is not a time-consuming exercise. In order to address the issues of persistent security threats in cloud computing paradigm, the proposed model includes computation, encryption and access to improve the overall security of cloud environment. However, these measures have been implemented, some of them can offer the best security when it comes to the ever-evolving threat. To maintain the privacy of clients’ data in the CL reinforcement learning, the paper suggests the use of homomorphic encryption for privacy-preserving RL inference. Therefore – as a result of the work – the necessity of implementing the suggested security model for the cloud computing environments as well as protecting the data and enhancing the level of trust in cloud solutions is stated. Keywords - QoS (Quality of Service), Energy Efficiency, Ontology, AAA (Authentication, Authorization, and Accounting), RSA Algorithm, RSA Algorithm, ReDCIM (Reconfigurable Digital Computing-In-Memory) Processor, tableopencache, LSTM (Long Short-Term Memory)

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.

Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0

The sixth generation of mobile networks (6G) has the potential to revolutionize the way we communicate, interact, and use information for machine-to-machine (M2M) communication in Industry 4.0 and Industry 5.0, while also improving coverage in places that were previously considered difficult to access and/or digitally excluded, and supporting more devices and users. The 6G network will have an impact through a combination of many technologies: the Internet of Things (IoT), artificial intelligence/machine learning, virtual and augmented reality, cloud computing, and cyber security. New solutions and architectures and concepts for their use need to be developed to take full advantage of this. This article provides an overview of the challenges in this area and the proposed solutions, taking into account the disruptive technologies that are yet to be developed.

A Review on Cloud Computing Security Issues, Applicable Solutions and Implementation

Cloud security refers to the methods and technologies used to protect data and resources stored in the Cloud. Cloud security is crucial as businesses increasingly adopt cloud computing to store and process sensitive business data. Effective cloud security solutions include encryption, access control, data loss prevention, and regular security audits. These methods ensure data confidentiality, integrity, and availability. A significant advantage of cloud security is that data can be backed up and protected off-site. This means that in case of any disaster or cyber-attack, important data remains secure and retrievable. Overall, cloud security is essential to ensure data privacy, protect against cyber threats and data breaches, and ensure business continuity.

Analysing Different Risk Management Approaches and Their Effectiveness in Enhancing Supply Chain Resilience

Purpose: This paper aims to review various risk management strategies as well as measure its capability as an instrument to strengthen the chain resilience in the backdrop of the present-day economic environment. Methodology: The paper utilizes well-known literature review techniques to gather and analyze the strategies including diversification, collaboration, technology adoption, and contingency planning that not only avoid supply chain risks but also create resiliency in organizations. It is focused on the root causes, effects, and aftermath of supply chain disruptions; the potential solutions to counter these problems are also evaluation by this process. Findings: The study points out that the traditional risk management approaches are not enough, and suggests implementing the techniques such as mapping of the supply chain, supplier diversification and investment in digital technologies like block chain, cloud computing and cyber security. These approaches aid in boosting the supply chain resilience by providing transparency, enabling backup inventory management, and improving collaboration and efficiency within the supply chain operations. Unique Contribution to Theory, Practice and Policy: The study complements the existing body of knowledge by offering a detailed overview of risk management strategies that can be implemented for the purpose of supply chain resilience. It provides with concrete suggestions on issues of diversity, the use of new technologies, and of coordination to strengthen the chains of supply against disruptions. Furthermore, the research calls for policy intervention to enable the utilization of the above-discussed strategies and increase supply chain resilience at a broader level as well.