Critical Factors for Personal Cloud Storage Adoption in China

Purpose： In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user＇s intentions.Design/methodology/approach： Based on technology acceptance model（TAM）, network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.Findings： Among the adoption factors we identified, network externality has the salient influence on a user＇s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.Research limitations： This study ignores time and cost factors that might affect a user＇s intention to adopt personal cloud storage.Practical implications： Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.Originality/value： This study is one of the first research efforts that discuss Chinese users＇ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

Organizational Data Breach:Building Conscious Care Behavior in Incident Response

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses.This research study draws upon the literature in the areas of information security,incident response,theory of planned behaviour,and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation.The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date,the 2018 SingHealth data breach.The single in-depth case study observed information security awareness,policy,experience,attitude,subjective norms,perceived behavioral control,threat appraisal and self-efficacy as emerging prominently in the framework’s applicability in incident handling.The data analysis did not support threat severity relationship with conscious care behaviour.The findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.

An Anomalous Behavior Detection Model in Cloud Computing

This paper proposes an anomalous behavior detection model based on cloud computing. Virtual Machines （VMs） are one of the key components of cloud Infrastructure as a Service （laaS）. The security of such VMs is critical to laaS security. Many studies have been done on cloud computing security issues, but research into VM security issues, especially regarding VM network traffic anomalous behavior detection, remains inadequate. More and more studies show that communication among internal nodes exhibits complex patterns. Communication among VMs in cloud computing is invisible. Researchers find such issues challenging, and few solutions have been proposed--leaving cloud computing vulnerable to network attacks. This paper proposes a model that uses Software-Defined Networks （SDN） to implement traffic redirection. Our model can capture inter-VM traffic, detect known and unknown anomalous network behaviors, adopt hybrid techniques to analyze VM network behaviors, and control network systems. The experimental results indicate that the effectiveness of our approach is greater than 90%, and prove the feasibility of the model.

Factors influencing academic library users' intention to use mobile systems: A comparison of current users and potential adopters

Purpose: The study intends to examine the factors influencing the behavioral intention to use academic libraries’ mobile systems from the perspective of current users and potential adopters, respectively. Design/methodology/approach: Our study investigates the mobile library system’s acceptance by using a context-specific extension of the theory of reasoned action(TRA) and the technology acceptance model(TAM), which includes such factors as mobile self-efficacy, personal innovativeness and perceived playfulness. Structural equation modeling was used to test the validity of the proposed model based on the empirical data which was collected from 210 questionnaire survey participants.Findings: The result shows that 1) for both current users and potential adopters, attitude toward use and subjective norm both have a significant and positive impact on behavioral intention to use; 2) perceived usefulness and perceived ease of use are significantly correlated to potential adopters’ attitude toward use whereas perceived usefulness and perceived playfulness are significantly related to current users’ attitude toward use; 3) as for the comparison between the two groups of users, personal innovativeness not only affects perceived usefulness of both current users and potential adopters, but also affects potential adopters’ perceived playfulness positively. Mobile self-efficacy has a significant effect on perceived ease of use for both types of users.Research limitations: Although the sample size met the basic statistics requirements for the social research, the participants were mainly college students, and other mobile system users like faculty members and researchers were not investigated. In addition, some influencing factors, such as information quality, system quality and service quality were not considered in the research model.Practical implications: This study reveals main factors which influence both current users and potential adopters’ intention to use the mobile system, providing academic libraries withinsights into management strategies to offer customized mobile services to different types of users. Originality/value: Previous studies did not distinguish current users from potential adopters, which is not conducive for academic libraries to provide customized services and attract potential users. We presented an exploratory study to address this issue.

Prediction-Based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

In a cloud computing environment, users using the pay-as-you-go billing model can relinquish their services at any point in time and pay accordingly. From the perspective of the Cloud Service Providers (CSPs), this is not beneficial as they may lose the opportunity to earn from the relinquished resources. Therefore, this paper tackles the resource assignment problem while considering users relinquishment and its impact on the net profit of CSPs. As a solution, we first compare different ways to predict user behavior (i.e. how likely a user will leave the system before its scheduled end time) and deduce a better prediction technique based on linear regression. Then, based on the RACE (Relinquishment-Aware Cloud Economics) model proposed in [1], we develop a relinquishment-aware resource optimization model to estimate the amount of resources to assign on the basis of predicted user behavior. Simulations performed with CloudSim show that cloud service providers can gain more by estimating the amount of resources using better prediction techniques rather than blindly assigning resources to users. They also show that the proposed prediction-based resource assignment scheme typically generates more profit for a lower or similar utilization.

面向高性能计算环境的多维自适应授权访问策略

高性能计算能力是国家综合实力和创新能力的重要体现,是支撑我国科技持续发展的关键技术之一。随着高性能计算的发展,越来越多领域的科研人员开始关注并使用高性能计算环境。高性能计算环境目前面临资源有限、用户数目增多等挑战。为保证环境的安全性、提高环境资源的利用率,需设置一定的授权访问策略来约束用户的访问行为。本文针对高性能计算环境服务对象用户和应用社区或业务平台,基于机器学习算法对用户行为进行分析获取相关属性,设计并实现了一种多维自适应授权访问策略(MAAC)。实验表明,MAAC可实现对环境资源有效和灵活访问控制,同时该策略的决策时间可控制在1 ms内,与策略响应时间相比可忽略不计。

基于云计算的线上商品智能推荐系统设计与应用

受用户行为和商品属性的影响,线上商品推荐的可靠性难以得到保障,为此,设计基于云计算的线上商品智能推荐系统。将密集计算型ic5云服务器作为系统的硬件装置;在软件设计阶段,利用云计算技术对用户行为进行综合分析,并将其与商品属性进行匹配分析,确定最终的推荐结果。应用测试结果显示,该系统在不同数据集上的接受者操作特性曲线下面积(Area Under Curve,AUC)表现出了较高的稳定性,且均在0.88以上,表明该系统具有较高的应用价值。

基于用户行为特征的网络教学云平台资源个性化推荐方法

针对网络教学云平台的数据量大导致推荐的相关度低的问题,文章提出基于用户行为特征的网络教学云平台资源个性化推荐方法。该方法通过采集历史数据,筛选关键特征,结合K-中心聚类算法挖掘用户行为特征,建立与资源特征关联规则,结合偏好因子,为用户生成资源个性化推荐列表。实验测试结果表明,该方法推荐相关度高,满足平台实际应用需求。

云环境下跨层级间歇故障行为建模与量化分析

间歇故障具有随机性,复杂的机理与表现会导致传统诊断方法不再有效,尤其是对于云计算环境,因为长时间运转、高负载运行以及集群规模大等因素致使间歇故障易感性居高。针对此问题,提出了一种基于跨层级间歇故障传播行为的建模与量化分析方法。该方法对间歇故障行为演化进行跟踪并量化分析,包括发现并记录故障在云环境不同层级的传播,构建传播路径,量化间歇故障对结构级、固件、超特权、特权和用户层级的影响,以及故障模型参数对系统影响的比较,以为云环境下间歇故障诊断策略提供辅助诊断信息和指导。

基于大数据驱动的电力营销用户行为分析

阐述通过收集和分析用户数据,如用电记录、消费习惯等,可以准确地了解用户特征、需求和偏好。借助大数据技术,从海量的数据中发现隐藏的规律,并将其应用于电力市场的营销策略中。

基于云计算的移动互联网大数据用户行为分析引擎设计

随着移动互联网的迅速发展,运营商面临的竞争日益激烈,流量经营势在必行,基于用户行为分析的精确营销是重要手段。但在大数据时代,随着移动互联网业务和用户数量的快速增长,传统的架构难以适应海量数据挖掘的要求。本文提出了一种基于云计算的移动互联网大数据用户行为分析引擎解决方案,包括系统总体架构设计、大数据入库与预处理组件、大数据用户行为分析模型等关键模块的设计,最后分析了系统测试效果。

基于云计算的居民用电行为分析模型研究

对智能小区的居民用电行为展开研究,基于云计算平台和并行k-means聚类算法,建立了峰时耗电率、负荷率、谷电系数等时间序列特征,并采用熵权法计算各类特征权重,实验数据来自已建的智能小区中的600名用户。实验结果表明,智能小区的居民用户被分成空置房、上班族、上班族+老人、老人家庭、商业用户等5类用户,聚类的准确率达到了91.2%,证明文中基于云计算平台和并行k_means聚类算法的居民用电行为分析模型是有效的。

社交媒体后续采纳阶段用户转移行为研究——以微信为例

近年来,由于社交媒体类型不断创新,市场竞争不断升级,用户对于社交媒体的黏性和依赖度成为学界和业界共同关注的话题。在社交媒体的后续采纳阶段,用户经常从一类产品转移到另一类产品,这种迁移行为背后蕴藏着复杂的动机和原因。鉴于此,了解影响用户转移行为的因素,对社交媒体提供商和产品经理有着重要的实际意义。本研究以微信为例,从人类迁移学研究中引入基于网络的人类迁移理论,以探讨虚拟世界的用户转移行为,构建了社会化媒体用户转移概念模型。在模型中,我们将用户转移分成转移意愿和实际转移行为两个过程,并从社会网络性、相对匮乏性、了解型信任和个人创新四个方面提出相关假设。其中相对匮乏性为形成性变量,由相对情感匮乏性、相对功能匮乏性和相对经济匮乏性三个反映性变量组成。本研究在对问卷的信度和效度检验的基础上,利用偏最小二乘方法工具WarpPLS 4.0对模型和相关假设进行了检验。研究结论对于社交媒体的交互设计和管理模式提供了参考价值,并从理论上深化了虚拟社区中用户转移行为的概念模型。

云制造环境下基于用户行为感知的个性化知识服务技术

为提高云制造平台中的个性化知识服务能力、增强用户黏度,提出一种云制造环境下基于用户行为的个性化知识服务方法。针对云制造服务特点,构建云制造环境下的个性化知识服务架构;分析了云制造平台中的用户行为,综合显性行为和隐性行为计算用户的个性化知识需求度,建立基于本体的用户行为模型;在此基础上,通过语义分析及知识评价实现了个性化知识过滤,通过应用示例分析验证了该方法的可行性。

基于行为的云计算访问控制安全模型

针对当前流行的云计算技术,分析了其所面临的安全问题。以BLP(Bell-LaPadula)模型和Biba模型为参考,通过基于行为的访问控制技术来动态调节主体的访问范围,实现BLP模型和Biba模型的有机结合,提出了CCACSM(cloud computing access control security model)。该模型不仅能保护云端服务器中数据完整性和保密性,而且使云计算环境具有相当的灵活性和实用性。最后给出了该模型的组成部分、安全公理和实现过程。

云计算环境下基于用户行为特征的资源分配策略

资源分配策略是云计算研究领域中的一项重要研究点,研究人员提出了多种资源共享与分配策略,然而很少有工作关注不同云计算用户群体的行为习惯对资源分配策略的影响.提出的基于用户行为特征的资源分配策略就是通过统计用户工作习惯与任务完成时间期望值的变化规律,建立用户行为特征信息表,从而预测出不同时间片内用户的任务提交规律以及用户期望完成时间,动态调整云计算系统的资源分配策略,使得系统在满足用户预期任务完成时间的前提下实现任务并发最大化,提升单位资源的用户满意度.HUTAF(Huawei unitfied test automation framework)云测试平台是华为公司自行研发的云测试平台,并基于该平台开展各种策略下的资源利用率与用户满意度实验.实验结果表明,该策略提升了整个系统在满足用户期望完成时间的前提下的总任务并发数,有效降低了IaaS供应商的运营成本.

云计算环境下基于动态博弈论的用户行为模型与分析

云计算环境下,开放的运行环境使其面临重大的安全挑战,有效地确定不可信云终端用户并正确分析云用户的异常行为是在复杂动态环境下保证云安全的基础.提出了一种基于动态博弈的用户行为模型,通过不完全信息多阶段博弈来分析终端用户的类型,博弈时将用户的当前行动和历史行动相结合,并考虑了网络中存在的误报和漏报的情况,以加强对云终端用户类型推断的准确性和全面性.理论证明和实验验证表明该机制能快速甄别系统中潜在的不可信云终端用户,有效遏制不可信云终端用户的侵入行为,为主动安全机制的实现奠定基础.

云计算环境下动态用户行为认证的机制、模型与分析

在云计算中,终端用户对云资源软硬件的影响和破坏远比目前用户利用因特网进行资源共享要严重的多,因此相对于用户的身份,用户的行为是否真实可信是云计算研究的一个重要内容。提出了户行为认证的机制,包括行为认证集的确立、行为证据的获得、行为认证的策略,建立相应的随机Petri网模型,并通过量化瞬时变迁的实施概率来模拟不同的认证选择,利用系统达到稳定状态时位置中的平均标记数来评价认证效果,为云计算的安全应用奠定理论和实践基础。