As cloud computing usage rises, ensuring secure data transmission has become crucial. This study addresses this need by proposing a multi-risk protection plan and a comprehensive cybersecurity trust model to enhance d...
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As cloud computing usage rises, ensuring secure data transmission has become crucial. This study addresses this need by proposing a multi-risk protection plan and a comprehensive cybersecurity trust model to enhance data transmission security in cloud environments. The objective is to develop a robust framework that improves stability, optimizes energy efficiency, and enhances Quality of Service (QoS). To achieve stability, we implement redundancy, failover mechanisms, and load balancing for efficient resource utilization. The cybersecurity trust model offers a dynamic framework to evaluate the reliability of cloud service providers, while the multi-risk protection scheme addresses various threats, including insider threats and data breaches, through a layered approach. Anomaly detection, intrusion detection systems, and encryption protocols further strengthen security. In our research methodology, we test the model in a simulated environment, replicating real-world data transmission scenarios. Performance metrics include throughput, latency, and risk detection accuracy. The findings demonstrate significant improvements in data transmission security, with our encryption method achieving an impressive time of 0.2 s. Overall, the multi-risk protection scheme effectively identifies and mitigates risks, promoting a more secure cloud computing ecosystem.
In cloud data centers, the exponential growth of data places increasing demands on computing, storage, and network resources, especially in multi-tenant environments. While this growth is crucial for ensuring Quality ...
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In cloud data centers, the exponential growth of data places increasing demands on computing, storage, and network resources, especially in multi-tenant environments. While this growth is crucial for ensuring Quality of Service (QoS), it also introduces challenges such as fluctuating resource requirements and static container configurations, which can lead to resource underutilization and high energy consumption. This article addresses online resource provisioning and efficient scheduling for multi-tenant environments, aiming to minimize energy consumption while balancing elasticity and QoS requirements. To address this, we propose a novel optimization framework that reformulates the resource provisioning problem into a more manageable form. By reducing the original multi-constraint optimization to a container placement problem, we apply the interior-point barrier method to simplify the optimization, integrating constraints directly into the objective function for efficient computation. We also introduce elasticity as a key parameter to balance energy consumption with autonomous resource scaling, ensuring that resource consolidation does not compromise system flexibility. The proposed Energy-Efficient and Elastic Resource Provisioning (EEP) framework comprises three main modules: a distributed resource management module that employs vertical partitioning and dynamic leader election for adaptive resource allocation;a prediction module using omega-step prediction for accurate resource demand forecasting;and an elastic scheduling module that dynamically adjusts to tenant scaling needs, optimizing resource allocation and minimizing energy consumption. Extensive experiments across diverse cloud scenarios demonstrate that the EEP framework significantly improves energy efficiency and resource utilization compared to established baselines, supporting sustainable cloud management practices.
The existing landscape of context-aware mobile applications reveals a significant gap in standardized approaches for knowledge representation, particularly in addressing the challenges posed by intelligence requiremen...
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The existing landscape of context-aware mobile applications reveals a significant gap in standardized approaches for knowledge representation, particularly in addressing the challenges posed by intelligence requirements such as automation, dynamism, and intelligent support. To address these critical issues, this study proposes the integration of Semantic-aware Service into cloud computing Architecture (SSCCA) as an innovative solution. SSCCA serves as a unified framework designed to empower intelligent context-aware mobile applications by leveraging a foundational cloud service model. By amalgamating principles from cloud computing and Semantic Web research, SSCCA aims to facilitate the development of advanced cloud computing applications with built-in intelligence. To validate the efficacy of this novel approach, we present the Smart Context-aware Invoice Platform (SCIP), a sophisticated cloud computing application built upon SSCCA. SCIP is engineered to aggregate personal electronic invoices seamlessly and deliver context-aware mobile services on demand, thereby addressing the pressing need for intelligence-driven solutions in the realm of mobile context-awareness.
This study recognizes the critical role of the cloud computing platform in scientific workflow applications yet identifies vulnerabilities in existing cloud workflow systems, such as information leaks, unauthorized ac...
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This study recognizes the critical role of the cloud computing platform in scientific workflow applications yet identifies vulnerabilities in existing cloud workflow systems, such as information leaks, unauthorized access, and compromised data integrity during task scheduling. Mainly, attackers exploit the lack of security for intermediate-level task information. To address these security threats, this work introduces the secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environments. The SEWS model identifies malicious attacks on all workflow tasks and focuses explicitly on safeguarding intermediary data. The SEWS model employs intelligent techniques to enhance security and introduces a comprehensive metric to measure the security of workflow tasks, considering factors like integrity, confidentiality, and availability. Beyond security improvements, the SEWS model aims to elevate the overall quality of service (QoS) in workflow scheduling applications. This includes reducing simulation time, enhancing overall power efficiency, and minimizing average energy consumption. Results: Results from the SEWS model demonstrate substantial improvements over the energy-minimized scheduling (EMS) model, with a reduction of 79.41% in average simulation time, 87.92% in average power sum, 41.35% in average power average, and 89.62% in average energy consumption. These findings underscore the SEWS model's effectiveness in providing enhanced security and improved QoS in cloud workflow scheduling. The overarching goal of this work is to contribute to developing a more secure and efficient cloud workflow scheduling system, aligning with the increasing demands for robust security measures and optimized performance in heterogeneous cloud environments. Findings: Compared to the energy-minimized scheduling (EMS) model, the findings of this study demonstrate that the secure and efficient workflow scheduling (SEWS) model yields superior outcomes across k
cloud storage services offer a scalable platform to store a large amount of data at a low cost. It attracts a large number of customers to outsource their data to the cloud. To manage the massive growth in the size of...
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cloud storage services offer a scalable platform to store a large amount of data at a low cost. It attracts a large number of customers to outsource their data to the cloud. To manage the massive growth in the size of outsourced data, cloud service providers employ deduplication, i.e., a technique to reduce space and bandwidth requirements by eliminating the upload and storage of redundant data. However, it poses the following severe security threat: "A malicious user who learns deduplication tag of the file, i.e., a small piece of information, can convince the server to allow access to the entire file". A proof of ownership (POW) concept was introduced that allows the server to challenge the user to prove that s/he owns the entire file. The existing state-of-the-art POW solutions are either not considering the complete file for determining the proof or not efficient in terms of I/O, communication, and computational overheads on the user. In this paper, we propose a secure and efficient POW scheme. The proposed scheme ensures that the user must possess the complete file to generate ownership proof. In addition, our scheme causes minimal I/O, communication, and computational overhead on the user side. We implement the proposed scheme in a real cloud scenario using Google Firebase cloud services. The performance analysis indicates that our scheme is efficient in terms of I/O, computational, and communication overheads than the existing state-of-the-art solutions.
Backgroundcloud computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabi...
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Backgroundcloud computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large-scale data centers. Therefore, cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large-scale *** paper proposes cloudSim 7G, the seventh generation of cloudSim, which features a re-engineered and generalized internal architecture to facilitate the integration of multiple cloudSim extensions within the same simulated *** part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the *** result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run-time performance and memory efficiency (up to 25\% less heap memory allocated), as well as increased flexibility, ease-of-use, and extensibility of the *** improvements benefit not only cloudSim developers but also researchers and practitioners using the framework for modeling and simulating next-generation cloud computing environments.
Collaborative searchable encryption for group data sharing enables a consortium of authorized users to collectively generate trapdoors and decrypt search results. However, existing countermeasures may be vulnerable to...
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Collaborative searchable encryption for group data sharing enables a consortium of authorized users to collectively generate trapdoors and decrypt search results. However, existing countermeasures may be vulnerable to a keyword guessing attack (KGA) initiated by malicious insiders, compromising the confidentiality of keywords. Simultaneously, these solutions often fail to guard against hostile manufacturers embedding backdoors, leading to potential information leakage. To address these challenges, we propose a novel privacy-preserving collaborative searchable encryption (PCSE) scheme tailored for group data sharing. This scheme introduces a dedicated keyword server to export server-derived keywords, thereby withstanding KGA attempts. Based on this, PCSE deploys cryptographic reverse firewalls to thwart subversion attacks. To overcome the single point of failure inherent in a single keyword server, the export of server-derived keywords is collaboratively performed by multiple keyword servers. Furthermore, PCSE extends its capabilities to support efficient multi-keyword searches and result verification and incorporates a rate-limiting mechanism to effectively slow down adversaries' online KGA attempts. Security analysis demonstrates that our scheme can resist KGA and subversion attack. Theoretical analyses and experimental results show that PCSE is significantly more practical for group data sharing systems compared with state-of-the-art works.
作者：
Hu, RongYang, XuelingGeely Univ China
Sch Intelligence Technol Chengdu 641423 Sichuan Peoples R China 123
Sec 2Chengjian Ave Chengdu Sichuan Peoples R China
Effective mining of relationships within massive medical datasets can profoundly enhance clinical decision-making and healthcare outcomes. However, traditional data mining techniques falter in extracting actionable as...
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Effective mining of relationships within massive medical datasets can profoundly enhance clinical decision-making and healthcare outcomes. However, traditional data mining techniques falter in extracting actionable associations from large-scale medical data. This research optimizes the Frequent Pattern Growth algorithm and incorporates it into a Hadoop framework for scalable medical data analytics. Empirical evaluations on real-world patient diagnosis records demonstrate the proposed approach's computational and learning efficiency. For instance, with the Break-Cancer database, the optimized algorithm requires just 0.04 seconds at 0.22 minimum support, significantly faster than existing methods. Experiments on diagnostics data generate 267 informative association rules at 0.31 support - markedly higher than 71, 126 and 233 rules produced by other comparative techniques. By enabling rapid discovery of data-driven health insights, the enhanced medical data mining framework provides a valuable decision-support system for better clinical practice. Ongoing explorations focus on further optimizations for automated disease prediction and treatment recommendations to continuously augment data-to-diagnosis applicability.
In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, consi...
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In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, considering deadlines and budgets while optimizing task allocation to virtual machines (VMs). Our study hypothesizes that improved scheduling can reduce energy consumption, streamline process execution, and lower operational costs. To test this hypothesis, we conducted a comparative analysis of two optimization techniques: Genetic Algorithm with Multiple Particle Swarm Optimization (GA + MPSO) and Genetic Algorithm with Bat Algorithm (GA + BAT). The analysis reveals that the combination of Genetic Algorithm and Bat Algorithm (GA + BAT) excels in optimizing cloud computing workflow scheduling. GA + BAT demonstrated superior performance by significantly reducing energy consumption, shortening process execution times, and decreasing operational costs. These findings validate our hypothesis, underscoring that optimizing cloud computing workflow scheduling can deliver substantial benefits. Consequently, by adopting GA + BAT, cloud service providers can enhance efficiency, reduce costs, and foster a more sustainable and responsive cloud infrastructure.
In order to facilitate diverse computing resources and services, cloud computing has evolve into a promising paradigm on-demand over the internet. To access services, cloud users have to rely on third-party service pr...
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In order to facilitate diverse computing resources and services, cloud computing has evolve into a promising paradigm on-demand over the internet. To access services, cloud users have to rely on third-party service providers. Choosing a suitable cloud Service Provider (CSP) with a raise in available cloud services in order to deliver the service safely is considered a serious concern for users. Regrettably, there are various problems that minimize the growth of cloud computing, like privacy, security loss, and control. The security issue is regarded as the most important element that could avoid the evolution of cloud computing. In the cloud environment, to handle the user's requests, trust measures play a significant role when choosing appropriate service providers. Therefore, trustworthiness evaluation of CSP prior to choosing it to facilitate the service has become a significant obligation in cloud environment. In this work, a trust model, Deep Behavioral Feedback Quality of Service and Statistics based trust (Deep BFQS-trust), is developed to calculate trustworthiness of CSP based on its feedback and behavior, QoS and statistics-based given by the users. Also, to calculate behavioral trust values, various QoS attributes are considered. In order to maintain and calculate feedback trust value for service provider, diverse parameters from service level agreement are utilized. By computing the collective trust, trustworthiness of cloud service provider is judged that is computed by these trust factors. Moreover, the weights of the collective trust are determined by employing Deep belief network (DBN) model. Finally, the proposed Deep BFQS-trust technique is compared with the existing approaches, and exhibits that the proposed model attained utmost trustworthiness and successful interaction with the values of 0.860 and 0.888, respectively.
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