An Effective Security Enhancement Scheme for Postquantum Cryptography Based Blockchain Networks in Cloud Computing

WITHDRAWN: Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing

Enhancement of Security in Cloud Maas and JUJU Using Foxiproxy

Cloud computing has transformed the way businesses and consumers think about their data and businesses. As a result, cloud computing is described as the on-demand availability of all computer system resources via the Internet as a paid service. Enhancing security is a major problem in cloud computing, which is also a major research topic because data is stored and processed in remote locations held by third parties. Another key research subject is the allocation of virtual machines to incoming workloads to decrease the cost of consumers' workload execution in cloud environments. Both of the aforementioned difficulties are addressed in this study. Deadline-constrained workflows are submitted to the application server, which goes through a pre-processing step, that identifies the presence of anomalies in the workflow tasks and disqualifies those tasks with anomalies, and schedules the adjusted workloads into heterogeneous Virtual machines using a modified-PCP algorithm. Our approach is compared to the IC-LOSS and IC-PCP algorithms that are already in use. In comparison to the existing IC-PCP and IC-LOSS algorithms, experimental results suggest that using the modified-PCP algorithm for deadline constrained workflows after deleting those anomalous tasks produces better results.

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ABSTRACTLive virtual machine (LVM) migration is pivotal in cloud computing for its ability to seamlessly transfer virtual machines (VMs) between physical hosts, optimise resource utilisation, and enable uninterrupted service. However, concerns persist regarding safeguarding sensitive data during migration, particularly in critical sectors like healthcare, banking and military operations. Existing migration methods often compromise between performance and data security, prompting the need for a balanced solution. To address this, we propose a novel framework merging machine learning with selective encryption to fortify the pre‐copy live migration process. Our approach intelligently predicts optimal migration times while selectively encrypting sensitive data, ensuring confidentiality and integrity without compromising performance. Rigorous experiments demonstrate its effectiveness, showcasing an average 51.82% reduction in downtime and an average 72.73% decrease in total migration time across diverse workloads. This integration of selective encryption not only bolsters security but also optimises migration metrics, presenting a robust solution for uninterrupted service delivery in critical cloud computing domains.

Cloud computing has created a dramatic change in the banking sector. It is now possible to do cost-efficient, scalable, and high-performance online transactions using the applications of cloud computing. In addition to improving data storage, processing speed, and security, cloud computing reduces costs used in infrastructure. High latency, security risks, and compliance issues mar the traditional banking systems. These limitations made traditional banking inefficacious for recent financial applications such as High-Frequency Trading. This paper proposes a secure and efficient cloud-based high frequency trading (HFT) system based on Lattice-Based Post-quantum Cryptography (LBPQC) and Partially Homomorphic Encryption (PHE) for the provision of more secure transactions. The system minimizes both latency and computational expenses, thereby facilitating real-time executions while improving trade precision by 15% and reducing security breach chances by 20%. AI-driven compliance automation and anomaly detection are also complemented into the model for regulatory compliance. Much higher data protection as well as efficiency in the trade, and threat detection are possible with the proposal that will ensure effective and secure banking infrastructure for any cloud financial activity.

As big data continues to proliferate at an unprecedented rate, cloud computing has emerged as a fundamental technology for managing, storing, and processing these vast datasets, with global data volumes projected to reach 175 zettabytes by 2025. Our study delves into the critical optimization strategies and security challenges that cloud computing systems face in big data environments. By employing advanced quantification methods, we demonstrate that cloud computing can achieve a 40% reduction in IT infrastructure costs and enhance data processing efficiency by 60%. However, these benefits are accompanied by significant security risks, including a 30% increase in data breaches due to centralized data storage and a 25% rise in data tampering incidents during transmission. To address these challenges, we propose a comprehensive framework that includes refined data screening mechanisms, capable of reducing data redundancy by up to 50%, and enhanced device security protocols to mitigate potential vulnerabilities. Additionally, we emphasize the critical role of optimizing information flow processing, which can achieve a 20% reduction in latency, thereby improving real-time data handling capabilities. Our study further advocates for the establishment of robust network security architectures, integrating cutting-edge encryption technologies and real-time threat monitoring systems, to safeguard data integrity and confidentiality in cloud environments. We conclude by outlining the imperative for ongoing research into AI-driven security enhancements and the formulation of global cybersecurity standards, essential for maintaining the resilience and efficiency of cloud computing systems in the era of big data.

Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).

Blockchain has proven itself to be of value in various fields of technology. In today's world every kind of technology implementation requires security, the research to make it better is constantly taking place. With such a requirement, blockchain has started to make a huge difference in the way security protocols are implemented. The data is stored in the form of blocks where in every one of them transactions are stored. Every block added connects itself to the existing blocks in a secure manner such that it is resistant to attacks and tamper. With emerging advances in IIoT, the requirement for better security has increased, and hence a permissioned blockchain security solution for IIoT has been proposed. In this paper, an additionally secure approach using cloud computing is used where the private keys provided to the user are broken down and stored on cloud is proposed.

Under the overwhelming trend in Cloud Computing, Cloud Databases possessing high scalability / high availability / high parallel performance have become a prevalent paradigm of data outsourcing. In consideration of security and privacy, both individuals and enterprises prefer to outsource service data in encrypted form. Unfortunately, most encrypted databases cannot support such complicated queries as wildcard-based fuzzy searching, which, to some extent, limits the practicability in real applications. To explore more business logic in encrypted databases, an enhanced wildcard-based fuzzy searching scheme (enWFS) is proposed in this paper, which integrates specialized Adjacent Character Matrix/Tensor into proxy middleware, appends two types of ancillary columns into data tables, as well as designs an advanced adaptive overwriting method to revise query expressions with wildcards (‘%’ and ‘_’). Meanwhile, some security enhancements and TupleRank are added to enWFS scheme so as to achieve superior fuzzy searching experiences. Extensive experiments based on real datasets demonstrate effectiveness, feasibility of our proposal.

Currently some of the most preferred models of technology delivery are cloud computing models of technology, which have dramatically transformed data management and storage but have also resulted in sophisticated risks. In this paper, we will analysis the development of cloud security threats, typical threats, and new threats in multi-cloud environments. Security enhancements are outlined, and the array of shortcomings of previous security solutions are presented, as well as prospects and challenges related to compliance. It is found that for combating these threats, there is need for implementational security measures, responsive technologies and standardized policies across the world. New ideas like zero-trust security and cryptographic forms are also stated to address the risks. This review’s implication, therefore, is that there should be partnerships in realizing safer and more robust clouds.

Blockchain Architecture: Enhancing Security in Cloud Computing

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With the rise of the day to day use of distributed systems in cloud computing and in enterprise networks, securing a means of communication and access to resources is a fundamental challenge. Traditional security mechanisms usually do not supply robust access control, traffic filtering, and network segmentation, all of which could help to counter cyber threats. This paper investigates how Bastion Hosts, Network Address Translation (NAT) Gateways and Network Access Control Lists (ACLs) are necessary security building blocks of distributed environments. The mechanisms we present are analyzed in detail with respect to how they work individually and together to deliver layered security. We then evaluate the effect of their execution on system latency, network performance, and overall security of the system via theoretical modeling and empirical evaluation. We show that such a multi layered approach of security of Bastion Hosts, NAT Gateways and Network ACLs drastically improves network resilience, eliminates points of attack surfaces and minimize unauthorized access risks. The study also gives mathematics formulations for security risk assessment and presents an optimized security architecture for distributed systems. Keywords: Distributed Systems, Bastion Host, NAT Gateway, Network ACL, Cybersecurity, Network Security, Access Control, Cloud Security

Ensuring the security and privacy of patient data in e‐healthcare systems that rely on cloud computation is of utmost importance. Traditional encryption is no longer resistant to quantum attacks and safeguards sensitive medical images. To tackle this issue, robust security countermeasures are proposed by integrating quantum encryption with a cloud‐based healthcare system. The encryption scheme utilizes the Generalized Novel Enhancement Quantum Representation (GNEQR) and the Novel Enhancement Quantum Representation (NEQR) to provide a framework for representing color and grayscale healthcare images. The proposed quantum algorithm uses quantum logic for image scrambling, which is combined with the encryption key by the Xor quantum gate. The encryption key is generated by 9D chaotic and permutated before encryption. Finally, channel re‐ordering is applied for color images. The simulation results for 15 medical tests with an encryption key space >2600 on a developed e‐healthcare system demonstrate the effectiveness and reliability of the proposed work where the average number of pixels change rates was 99.82, while the unified average change intensity rate was 33.51, entropy was 7.9, the horizontal, vertical, and diagonal correlation coefficients averaged 0.000533333, 0.000706667, and 0.00076, respectively. Finally, the mean squared error (MSE) between the original and encrypted images was 10203.72. These findings improve digital healthcare by revealing the solutions' performance, security, and efficacy.