Cloud Security Research Articles

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.

SecureCloud Guardian: Advancing Data Security via Advanced Dispersion and Encryption in Cloud Strorage (SCG)

Cloud storage, being one of the foundational pillars of modern computing, has been readily embraced and spurred fast growth in the cloud computing domain. Despite this massive and widespread acceptance, major security issues hang over the cloud storage scene and often result in huge security breaches and leaks of information. SecureCloud Guardian, or SCG, comes into the picture as an important instrument that guarantees privacy in information stored over the cloud. SCG follows a multi-pronged approach, including data dispersal techniques, storage distribution protocols, and encryption formats that guarantee efficient data storage. It also employs a hierarchical management approach that leverages user-level secret code as well as secret division algorithms for maintaining cryptographic material, preventing unauthorized access. The experimental studies carried out on SCG verify the effectiveness of this security-centered cloud storage technology in terms of avoiding data leakage. For instance, SCG uploads and downloads 5G files in a mere 646 seconds and 269 seconds, respectively—an attractive choice for consumers looking for secure cloud information storage.

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.

Blockchain With Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography for Securing Image

ABSTRACTNowadays, the image security is one of the most challenging issues to address the technological age. Security is the primary issue in data management and transmission because of the original data form that is read, abused and destroyed. The cloud companies struggle to secure the file. The cloud security is the major concern in cloud computing context. Numerous researches have been presented so far to protect the cloud environment. But, none of them provides the sufficient security. Therefore, this paper proposes a Blockchain‐based technique for Image Security that combines Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography (BC‐SIE‐HAPCNN‐FODCE). The Flickr30k dataset is used to collect the input images. At that point, cryptographic pixel values of picture are kept on blockchain to defend security of picture information. It uses Delegated Proof of Stake Consensus (DT‐DPoS) approach appointed confirmation of stake agreement approach. The performance parameters, like processing time, reaction time, runtime, correlation coefficient analysis, entropy analysis, mean square error, and availability are used to determine the efficacy of the proposed BC‐SIE‐HAPCNN‐FODCE approach. The performance of the proposed technique attains 18.81%, 32.05%, and 22.28% higher correlation coefficient and 25.38%, 20.81%, and 26.04% higher entropy compared with existing methods, such as Multiple Rossler lightweight Logistic sine mapping dependent Federated convolutional method with cyber blockchain in medical image encryption (BC‐SIE‐FCAL‐MRLLSM), color image encryption under Hénon‐zigzag map with chaotic restricted Boltzmann machine over Blockchain (BC‐SIE‐CRBM‐HZM) and blockchain‐assisted safe picture transmission along detection method on Internet of Medical Things Environment (BC‐SIE‐ECC‐DBN), respectively.

Ransomware Mitigation for Secure and Smart Cloud Manufacturing Using GeniLeaf Decision Tree with Load Balancing

Background: Cyber-attacks related to ransomware have increased significantly in cloud manufacturing industries over the last decade, causing considerable disruptions to organizations. This type of information may also include personal details, patent rights, bank account details, etc. This type of malware requires new and better mitigation methods. Objective: The primary objective of this study was to provide an algorithm to execute experiments using genetic algorithms for load balancing with decision trees and mitigate ransomware. Methods: Hybrid analysis and machine learning techniques were used in this study to identify ransomware. Since a wide range of samples impacted by ransomware share most of the characteristics, it may be possible to use this study to detect current and future malware variants in industries. Results: In patented industrial technology, ransomware mitigation plays a crucial role based on the analysis of various papers and patents-to-science references. In this study, a machine learning mitigation algorithm, GeniLeaf Decision Tree (GLDT), was applied to a featured dataset using a genetic algorithm with a decision tree Conclusion: Machine learning and load balancing are used to gain insight into ransomware behavior. By using the proposed approach to mitigate ransomware and spoofing patterns, a high level of accuracy is achieved. Using GeniLeaf Decision Trees to mitigate ransomware is a significant innovation.

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.

Cloud Computing Security

Cloud computing has transformed the IT landscape by providing scalable resources and services over the internet. However, this paradigm shift raises important security issues that need to be addressed to ensure data integrity, privacy and availability. This summary provides an overview of the primary security challenges in cloud computing, including data breaches, unauthorized access rights, and shared infrastructure vulnerabilities. It examines the various strategies and technologies used to mitigate these risks, such as encryption, multi-factor authentication and robust monitoring systems. In addition, he examines the role of compliance and standards in improving cloud security. By reviewing the theoretical and practical aspects of cloud security, this summary aims to highlight current efforts and future directions in fortifying cloud computing environments against emerging threats. Keywords — Cloud Computing, scalable, security issues, shared infrastructure, data breaches.

A Block-chain Based Mechanism for Securely Storing Data on Cloud and IOT

The burgeoning paradigm of cloud computing has made a number of tools available to researchers. It is a cutting-edge technology that keeps revolutionizing many sectors in every imaginable method. It is a promising technology that gives users and researchers new perspectives, development, and many amazing factors. The integration of cloud and block-chain technology and internet of things (IOT). The benefits both service providers and customers by enabling the use of resources that can be authenticated while protecting anonymity. This joining method is distinct and environmentally. This cooperative method sheds light on the different paths or facets of computers that can solve issues other than security-related ones. The primary problem at the moment is cloud security. For service providers, it's critical to protect client privacy and avoid data loss. Using client data privacy solutions, the current review study focused on block-chain with combination of IOT and data security. In order to strengthen security and expand the power or utility of cloud systems, a comprehensive examination is also carried out. Block-chain technology has the ability to increase speed and anonymity while providing numerous advantages to cloud-based and IOT based applications.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Block Chain-Based Approach towards a Trustworthy Cloud Computing

Cloud computing remains one of the most significant advancements in resource provision to organizations in the latest decades since they initiate conveniences on demand access. The adoption of the IoT has brought about a new era of collaborative computing by employing a pool of linked smart sensors and devices that will produce and analyze great amounts of data, thus creating new related problems in terms of size and security, hence increasing even more the importance of traditional security measures. Therefore, in this paper, we synthesize a new SSCA that combines IoT and cryptographic mechanisms, to build highly available and secure cloud systems, which makes these systems multi-users and fully usable by many users at the same time. The design resolves to a distributed structure where sever al cloud nodes will address user s requests proficiently and it uses Multicast and Broadcast Rekeying Algorithm (MBRA) to maintain the privacy and the confidentiality of the users’ detail, employing a cryptosystem that integrates MBRA, Post Quantum Cryptography (PQC) and block chain. The architecture using IoT devices collects data from the distributed sensing resources through the data storage layer; it enforces the security integrity of the information collected by employing the MBRA-PQC encryption algorithms; the blockchain provides the security for the confidential data by storing it in unalterable and dispersed registers. The proposed approach is then demonstrated with different data sets and its performance is measured based on the response time, the throughput and scalability, security and reliability. The outcome of the expressions reveals the efficiency of the proposed SSCA compared to MHE-IS-CPMT as it reduced by 1. 67 seconds and 0. 97 seconds at 250 and 1000 devices respectively. Similarly, the result as AUC value for SSCA showed a better improvement than the MHE-IS-CPMT, EAM, SCSS, and SHCEF models with improvements in percent at 25-user level: 6. 30%, 6. 90%, 7. 60%, and 7. 30%, respectively as well as; at 50-user level: 5. 20%, 9. 30%, 11. 50%.

I-MPaFS: enhancing EDoS attack detection in cloud computing through a data-driven approach

Cloud computing offers cost-effective IT solutions but is susceptible to security threats, particularly the Economic Denial of Sustainability (EDoS) attack. EDoS exploits cloud elasticity and the pay-per-use billing model, forcing users to incur unnecessary costs. This research introduces the Integrated Model Prediction and Feature Selection (I-MPaFS) framework to address EDoS attacks. I-MPaFS framework enhances an existing dataset to improve performance, using the generated data to build a Random Forest model for EDoS detection. Our investigation employs the UNSW-NB15, CSE-CIC-IDS18 and NSL-KDD datasets, demonstrating the proposed method’s superiority over existing techniques. The model achieved recall scores of 99.45% on the UNSW-NB15 dataset, 98.19% on the CSE-CIC-IDS18 dataset, and 99.82% on the NSL-KDD dataset, highlighting its reliability and efficacy in safeguarding cloud users from financial exploitation. This study contributes to the field by evaluating current EDoS detection methods, introducing the I-MPaFS framework, validating its performance with benchmark datasets, and comparing its effectiveness against state-of-the-art techniques. The findings affirm the significant potential of I-MPaFS in enhancing cloud security and protecting users from EDoS attacks.

Post-Quantum Cryptography for AI-Driven Cloud Security Solutions

This article presents an innovative framework combining post-quantum cryptography with AI-driven automation to address the imminent threat quantum computing poses to current cloud security systems. With the global cloud computing market projected to reach $1,554.94 billion by 2030 and 94% of enterprises using cloud services, the need for quantum-resistant security solutions is critical. The proposed framework integrates lattice-based cryptography, hash-based signatures, and multivariate cryptography with AI-driven security automation to provide a scalable, adaptable, and future-proof security solution. Initial tests demonstrate the framework's ability to process 5,000 encryption tasks per second while maintaining 99.9% uptime. The article explores the framework's methodology, evaluation results, practical applications across government, finance, and healthcare sectors, and future research directions. By leveraging advanced cryptographic techniques and AI, this framework aims to revolutionize cloud security in the quantum era, ensuring long-term data protection and system resilience.

The Impact of AI on Secure Cloud Computing: Opportunities and Challenges

This paper explores the intersection of Artificial Intelligence (AI) and cloud computing, focusing on the security implications. As cloud computing becomes increasingly ubiquitous, the integration of AI presents both opportunities and challenges. This paper provides an in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security. Key topics include AI's role in threat detection, data protection, access management, and the challenges related to AI bias, interpretability, and adversarial attacks.

Cloud Guardian: A Comprehensive Approach to Cloud Security Posture Management with Automated Multi-Cloud and Container Security

As organizations increasingly adopt multi-cloud environments and containerized applications to improve scalability and operational efficiency, managing security across these diverse platforms has become a critical challenge. Traditional security tools and manual processes are insufficient to handle the complexity and dynamic nature of modern cloud infrastructures. This research introduces Cloud Guardian, a cloud security posture management (CSPM) platform that automates security assessments, policy enforcement, and threat detection across multi-cloud platforms and containerized environments. Cloud Guardian addresses the key security challenges associated with multi-cloud and container environments, including misconfigurations, unpatched vulnerabilities, and inconsistent security policies. By integrating real-time threat intelligence and automated remediation capabilities, Cloud Guardian provides continuous protection, ensuring compliance with industry standards such as CIS and NIST. Additionally, the platform’s self-healing mechanisms and rollback capabilities allow for secure and uninterrupted operations by automatically fixing security issues and reverting changes if necessary. This study evaluates Cloud Guardian's effectiveness in securing complex cloud infrastructures through real-world case studies. The findings demonstrate significant improvements in operational efficiency, compliance, and security posture, reducing the risk of breaches and operational disruptions. Cloud Guardian offers a scalable and automated solution for organizations seeking to safeguard their cloud and container environments while minimizing manual intervention and ensuring continuous security and compliance.

A Study on Cybersecurity Challenges and Strategies in a Digitally Transformed Business Environment

In this era of digital transformation businesses are facing a rapidly changing cybersecurity landscape with new technologies, expanded attack surfaces and advanced threats. This article looks at the many cybersecurity challenges that arise from integrating advanced technologies like cloud, IoT and AI into business. The challenges are expanded attack surfaces, system complexity, data privacy and regulatory compliance and advanced threats. The article looks at the solutions to these challenges including Zero Trust architecture, layered security, advanced threat detection, incident response and security awareness training. plus real world case studies of how these solutions are being used and what’s coming next with emerging technologies and regulations. By addressing these challenges with a comprehensive approach businesses can strengthen their cybersecurity, protect their data and be resilient in a digital world. KEYWORDS Cybersecurity Challenges ,Digital Transformation, Cyber Threats ,IT Infrastructure ,Zero Trust Security ,Threat Detection ,Incident Response, Data Protection ,Regulatory Compliance Employee Training, Advanced Security Technologies, Cloud Security, Network Security

Retraction Note: Enhancing cloud security using crypto-deep neural network for privacy preservation in trusted environment

Retraction Note: Enhancing cloud security using crypto-deep neural network for privacy preservation in trusted environment

Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discussion on challenges, future research directions, and the need for optimization in real-time applications.

Secure and Efficient Data Hiding in the Cloud with Quantum-Resistant Encryption

Traditional steganographic methods in multi-cloud environments face significant challenges in cloud security, including complex key management, high computational overhead, and vulnerabilities to various attacks regaring secure data concealment. This paper presents a novel steganographic mechanism tailored for single cloud environments that address these issues. The proposed mechanism leverages a combination of homomorphic encryption, elliptic curve cryptography (ECC), and lattice-based cryptographic techniques to ensure robust security against classical and quantum attacks. Confining operations to a single cloud simplifies credential and key management, reducing operational complexity and enhancing system efficiency. The proposed approach mitigates the risks associated with multi-cloud environments and offers strong resistance to brute-force, differential, and steganalysis attacks. The implementation and validation of the mechanism demonstrate its efficacy in maintaining data integrity, confidentiality, and availability while minimizing computational resources. The results indicate that this approach significantly improves the security and performance of steganographic operations in cloud storage environments, setting a new standard for secure data concealment in cloud computing.

An IoT Cloud Security System Powered by Blockchain for Secure Preservation of Electronic Health Records

An IoT Cloud Security System Powered by Blockchain for Secure Preservation of Electronic Health Records

Tweakable ForkCipher from Ideal Block Cipher

In ASIACRYPT 2019, Andreeva et al. introduced a new symmetric key primitive called the forkcipher, designed for lightweight applications handling short messages. A forkcipher is a keyed function with a public tweak, featuring fixed-length input and fixed-length (expanding) output. They also proposed a specific forkcipher, ForkSkinny, based on the tweakable block cipher SKINNY, and its security was evaluated through cryptanalysis. Since then, several efficient AEAD and MAC schemes based on forkciphers have been proposed, catering not only to short messages but also to various purposes such as leakage resilience and cloud security. While forkciphers have proven to be efficient solutions for designing AEAD schemes, the area of forkcipher design remains unexplored, particularly the lack of provably secure forkcipher constructions. In this work, we propose forkcipher design for various tweak lengths, based on a block cipher as the underlying primitive. We provide proofs of security for these constructions, assuming the underlying block cipher behaves as an ideal block cipher. First, we present a forkcipher, F ~ 1 , for an n -bit tweak and prove its optimal ( n -bit) security. Next, we propose another construction, F ~ 2 , for a 2 n -bit tweak, also proving its optimal ( n -bit) security. Finally, we introduce a construction, F ~ r , for a general r n -bit tweak, achieving n -bit security.