基于Hadoop架构云平台的动态行为信任评估方法

针对Hadoop架构云平台不能验证"合法"用户的行为是否可靠的问题,文章提出了基于Bayes均值信任模型和动态行为信任链,用于计算每个客户端的信任值,使得信任值满足阈值的用户获得相应服务资源。该方案能够防止合法客户端受到"被动"的拒绝服务攻击,提高了客户端获取服务资源的公平性,同时有效防止恶意节点的协同欺骗。     

云计算环境下基于评价可信度的动态信任评估模型

针对云用户如何选取可信的云服务提供商问题,提出了基于评价可信度的动态信任评估模型。该模型将云服务提供商的服务能力和云用户所需求的服务能力分别划分等级,有效地解决了云服务提供商服务能力动态变化对模型存在的潜在破坏问题。建立了信任度随时间窗变化的动态信任机制,在计算信誉度时,将用户的评价可信度作为其评价证据的可信权重,通过引入评价可信度和评价相似度提高了计算推荐行为可信度的准确率。仿真结果表明,该模型的评估结果更贴近云服务提供商的真实信任度,同时能有效抵御恶意云用户的攻击。     

基于信任合成的云服务动态组合机制研究

云计算为代表的新型计算模式以灵活的"服务合约"为核心商业特征,通过动态整合各类云服务为用户提供不同粒度的增值服务.但是传统以QoS（Quality of Service,服务质量）为核心约束的服务组合方法,无法满足用户对服务服务质量的深层要求,即对服务信任程度的规范.为此本文提出了一种基于信任合成的云服务动态组合方法,该方法通过定义云服务的信任属性,将其分解为基础信任和经验信任的集合.将基础信任评价问题建模为云服务属性判断问题,利用Bayes合情推理分析了属性不可穷举情况下基础信任的审定与反驳.将经验信任评价问题建模为云服务交互行为判断问题,利用Chebyshe和Bernstein定理给出经验信任的置信度,进而为经验信任的量化提供依据.实验结果表明,本文所提出的方法可以在持续变化的云环境下有效地组织和提供云服务,进而满足新型计算模式动态多样化应用需求.     

一种集成直觉模糊信息的激励自适应信任模型

针对现有信任机制不能很好表达信任的不确定性,且缺乏有效处理分布式网络中存在的不诚实推荐和策略性欺骗等问题,该文提出一种集成直觉模糊信息的自适应信任评估模型。为了激励提供可信服务的实体,惩罚不良行为实体,该模型结合服务持续性强度和时间衰减因子,计算实体直接信任直觉模糊数。同时利用实体的推荐可信度和行为一致性过滤不诚实推荐行为。除此之外,为了克服综合信任聚合计算中权重确定较主观的问题,给出了一种新的自适应权重分配方法。仿真实验表明,该模型在提高实体服务积极性和抑制恶意欺诈行为中,都有较好的适应性和有效性。     

云计算环境下基于信任推理的服务评价方法

通过服务直接交互中的服务行为来建立SC与SP实体的信任推理、演化及对SP实体的QoS评价模型。该模型基于概率密度函数来表征服务实体间的信任度,提出了基于概率密度信任关系的计算、推理及合并的演化方法,并给出了依据信任推理与QoS修正的服务评价算法。实验结果表明该方法能有效评价服务参与者的信任度,削弱不可信的实体对服务评价的影响,提高服务选择过程的质量及准确性。     

移动云计算中的信任建立问题研究

信任建立的目标是确保数据的可信、真实、可靠和忠诚。在移动云计算中,由于数据存储和数据处理是在云端以远程方式进行的,因而信任是移动云计算安全中一个非常重要的参数。文章讨论移动云计算中的信任含义、特征、类型,研究弱信任链、控制和可视化缺失等信任相关问题,探讨服务级协议、审计、测量与评级、自评问卷、信任和声誉模型等5类信任建立方法,最后分析黑盒、由内向外以及由外向内等信任评价方法。     

基于动态邻接信任模型的安全路由算法研究

对现有路由节点信任相关问题进行了研究,综合路由节点的状态和行为因素提出了一种路由节点动态邻接信任模型。在此模型基础上提出了一种基于动态邻接信任熵的安全路由算法,并在现有OSPF路由协议中对该路由算法进行了验证。仿真结果表明提出的动态邻接信任模型能够准确地反映路由节点状态改变和恶意攻击,具有良好的动态响应能力,提出的安全路由算法能有效地保证路由节点的行为及状态可信并且具有良好的抗攻击性能。     

云计算环境下基于信任演化及集合的服务选择

针对云计算环境中服务节点的可信度参差不齐常导致用户很难获得高质量组合服务的问题,提出了一种基于信任生成树的云服务组织方法,将服务提供者与请求者的交互行为经演化后形成信任关系,使主体间可信程度达到相应级别,形成对外提供相似服务功能的云服务集合,将恶意、虚假的服务排除在信任生成树之外,使服务组合在可信场景中进行;在此基础上,采用了基于信息熵的度量策略来对服务间的信任关系进行评估,解决了现有研究中仅对可信参数进行简单加权分析的不足。实验分析表明,该方法能有效抑制云计算环境下恶意节点的欺诈行为并保护真实节点的合法利益,具有较好的服务选择质量。     

网格环境下信任体系结构建模

为了提高网格的安全性,针对网格环境的动态特点,首先给出网格环境下信任和信任关系等概念的基本定义,在此基础上提出信任评价和动态信任的概念,并构建了一个基于动态信任的信任体系结构模型,同时设计了该体系结构所涉及的4个基于信任的安全算法。最后,通过仿真分析验证了该模型能够增强网格系统的安全。     

基于私有云信任度量的对等网络节点安全互联模型

在缺乏必要信任关系的对等网络环境中,节点的安全互联对系统的稳定运行具有重要意义.针对对等网络存在的信任缺陷,借助私有云平台在计算与存储过程中的高效性、安全性等特点,提出一种私有云协助的对等网络信任度量模型.该模型基于主观逻辑理论对连接过程中的节点可信度进行度量,针对网络环境中存在的低质量服务、节点虚假推荐及策略性欺骗等问题开展研究,提出了应对机制.实验仿真结果表明,提出的模型具有较好的可行性、有效性和动态适应性.     

Dynamic game and reliable recommendation based transferring reputation mechanism for mobile cloud computing

The booming development of the mobile internet and cloud computing leads to the emerging of many mobile cloud platforms based services.However,since mobile users store lots of data and privacy information in the cloud when they are using the mobile cloud services,they are facing multiple increasingly serious security threats such as data leaks and privacy exposures.The data security and privacy protection was investigated in mobile cloud computing,aiming at the internal bad mouthing attacks and mobile attacks.A dynamic game and reliable recommendation based transferring reputation mechanism was proposed.First,a dynamic game based recommendation incentive mechanism was proposed.Secondly,a reliable recommendation reputation evaluation model was established based on the incentive mechanism.Last,a novel transferring reputation mechanism was proposed that combined the above mentioned incentive mechanism and reputation evaluation model.Simulation results demonstrate the proposed transferring reputation mechanism can defend against the internal bad mouthing attacks and mobile attacks effectively,enhance the credibility of mobile terminals and improve the data security and privacy protection of mobile cloud services.     

Hybrid model for dynamic evaluation of trust in cloud services

Cloud computing, a new paradigm in distributed computing, has gained wide popularity in a relatively short span of time. With the increase in the number, functionality and features of cloud services, it is more and more mind-boggling for the cloud users to find a trustworthy provider. Cloud users need to have confidence in cloud providers to migrate their critical data to cloud computing. There must be some means to determine reliability of service providers so that users can choose services with the assurance that the provider will not act malignantly. An effort has been made in this paper to formulate a hybrid model to calculate the trustworthiness of service providers. Cloud services are evaluated and trust value is calculated based on compliance and reputation. Service logs based compliance reflects dynamic trust. The reputation has been computed from collective user feedback. Feedback rating is the view of each user about the invoked services. The discovered services that fulfill the user requirements are ranked based on their trust values and top-k cloud services are recommended to the user. The proposed approach is efficient and considerably improves service-selection process in cloud applications.     

Research on cloud computing users’ public safety trust model based on scorecard-random forest

Traditional cloud computing trust models mainly focused on the calculation of the trust of users’ behavior.In the process of classification and evaluation,there were some problems such as ignorance of content security and lack of trust division verification.Aiming to solve these problems,cloud computing users’ public safety trust model based on scorecard-random forest was proposed.Firstly,the text was processed using Word2Vec in the data preprocessing stage.The convolution neural network (CNN) was used to extract the sentence features for user content tag classification.Then,scorecard method was used to filter the strong correlation index.Meanwhile,in order to establish the users’ public safety trust evaluation model in cloud computing,a random forest method was applied.Experimental results show that the proposed users’ public safety trust evaluation model outperforms the general trust evaluation model.The proposed model can effectively distinguish malicious users from normal users,and it can improve the efficiency of the cloud computing users management.     

MTBAC:云计算环境中一种基于互信任的访问控制模型（英文）

As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure the security of cloud computing.But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing.In cloud computing environment,only when the security and reliability of both interaction parties are ensured,data security can be effectively guaranteed during interactions between users and the Cloud.Therefore,building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment.Combining with Trust Management(TM),a mutual trust based access control(MTBAC) model is proposed in this paper.MTBAC model take both user's behavior trust and cloud services node's credibility into consideration.Trust relationships between users and cloud service nodes are established by mutual trust mechanism.Security problems of access control are solved by implementing MTBAC model into cloud computing environment.Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.     

基于机制设计理论的云计算SLA响应时间优化

针对云计算SLA中响应时间这一衡量云服务质量的重要指标,本文提出了一种DSIC(占优策略激励兼容)机制.在所有云资源提供商都是理性的这一共同知识假定下,DISC机制能保证云资源提供商显示真实的资源成本信息,云服务提供商以此为前提选择预算约束下能够在最短时间内完成用户任务的云资源提供商,从而达到优化用户服务响应时间的目的.本文对该机制的性能进行了严格证明,最后提出了一种寻找最优资源提供商组合的算法.     

云环境下服务信任感知的可信动态级调度方法

以社会学中的人际关系信任模型为基础,提出了一种基于服务消费者的服务满意度评价、推荐者的服务推荐和第三方服务性能反馈的可信度量模型。将用户对服务资源的信任需求和服务资源的可信度并入DLS算法得到可信动态级调度算法CTDLS,从而在计算调度级别时考虑服务资源的可信程度。模拟实验表明,该算法能有效满足任务在信任方面的服务质量需求,对提高任务调度的成功率具有实际意义。     

基于刺激机制的云服务定价策略

云服务定价问题是云计算商业化的关键,一个良好的定价策略能够给云服务供应商带来更多的收益,现有的云定价研究较少,定价模型还不成熟。笔者在分析了云定价研究现状的基础上,提出了一种基于刺激措施的定价方法,通过价格计算和盈利计算,决定刺激机制,一方面有利于避免资源浪费,另一方面有利于增强供应商的市场竞争力。     

基于信任的普适计算服务选择模型

借鉴人类社会的行为规律,提出普适计算中信任关系的建立、信任度的衡量、以及基于信任的服务选择模型;并依据信任的滞后性和服务选择前瞻性矛盾的动态演化规律,通过多角度(直接、间接)评估、喜好偏差调整和信任更新等综合手段,构造一套较健全的信任评估体系,以提高服务选择模型的准确可靠性。