基于交互感知的动态自适应的信任评估模型

构建了一种基于交互感知的动态自适应信任评估模型,将历史交互窗口和可信推荐数引入到了总体信任评估中,克服了传统模型对交互证据感知能力不足的问题。提出了基于满意度迭代的直接信任积累方法,并采用实体稳定度实现了激励和惩罚2种迭代策略,有效抑制了恶意伪装实体的作弊行为。给出了一种基于直接和间接相结合的综合推荐信任聚合方法,通过引入实体熟悉度和评分相似度解决了传统模型推荐准确度低和不可靠的问题。实验结果表明,与已有模型相比,该模型有效地提高了信任评估的准确性,并具有更强的抵御串谋实体协同作弊的能力。     

基于模糊Petri网的信任评估

针对网络实体间信任关系的不确定性,论文提出了一种基于模糊Petri网的信任评估模型,该模型给出了信任评估的模糊规则,在此基础上利用模糊Petri网与矩阵运算相结合的推理方法,通过并行处理关于一个实体的多个推荐来评估实体间的信任关系。经信任关系评估实例计算可以看出,基于模糊Petri网的推理方法使得信任评估灵活、简单、有效。     

P2P网络中具有激励机制的信任管理研究

针对现有的信任模型不能很好地处理P2P网络环境中存在的不诚实推荐、协同作弊与复杂的策略性欺骗等问题,提出了一种具有激励效果的分布式P2P信任管理模型(IMTM),以量化和评估节点的可信程度,并给出了模型的数学表述和分布式实现方法。信任模型利用相似度函数构建的推荐可信度模型作为节点推荐的准确性度量,并充分考虑节点行为的稳定性、可信服务持续强度、风险性及信任的时间相关性等因素。除此之外,为了有效激励节点积极提供高质量的服务,惩罚不良行为节点,还提出一种基于节点信任度的激励机制。分析及仿真实验表明,与已有的信任模型相比,IMTM在抑制更广泛的恶意节点攻击行为中,表现出更好的适应性、灵敏性与有效性。     

一种防节点策略性欺诈行为的网格信任模型

介绍了网格环境下一种可以有效防止节点策略性欺诈行为的信任模型.该模型通过引入"服务级别"和"推荐可信度"两个概念,可以有效的防止单个和多个实体的策略性欺诈行为.模型把网格分成不同的域,分别给出了域内和域间信任关系的计算和更新方法.     

证据信任模型中的信任传递与聚合研究

现有信任模型在信任传递和聚合方面存在一定的不足:首先是信任传递过程中缺少对直接推荐实体的反馈信任度的有效度量;其次是信任聚合过程中因缺少对推荐链之间依赖关系的有效处理而存在推荐信息的损耗或重复计算等问题。为了解决这些问题,提出了一种新的证据信任模型,结合D-S证据理论和图论方法有效解决了信任传递的可靠性和信任聚合的准确性等问题。仿真实验表明,与已有模型相比,所提模型具有更强的抑制各种策略欺骗及共谋行为的能力,在信任度量准确性方面也有较大提高。     

一种基于实体上下文和时间戳的信任预测模型

信任是网络计算模式下实体交互与协同的基础,如何准确地定量表示和评估信任是研究重点。该文提出以实体上下文和时间戳为条件的信任预测模型,建立了粒度为8的信任等级空间,引入了多维测量指标度量实体交互满意度,使得满意度计算更加精确。构建了具有时间衰减性的直接信任求解方法,克服了已有模型动态适应能力不足的问题。把推荐信任划分为直接推荐和间接推荐,在直接推荐信任求解中引入实体评分相似度因子,在间接推荐信任计算中提出了基于路径衰减的方法。提出了一种分布式树型存储机制DST(Distributed Storage Tree),提高了模型的稳定性和可扩展性。模拟实验表明,与已有同类型模型相比,该模型更有效和准确地提供决策依据,并且在抑制恶意实体方面具有明显作用。     

开放分布系统安全中的Bayes信任模型

为了解决开放分布系统的安全问题,本文提出了一个Bayes信任模型.借鉴人类的信任概念,该模型根据直接经验和推荐信息可以计算出一个实体对其他实体的信任值.在该模型中,把实体间交互成功的概率作为信任的重要指标,选择Beta分布作为其先验分布,通过对实体间4种不同关系的分析,获得相应的成功交互概率的估计.最终可形成实体的相对稳定的交互系统,为是否和别的实体交互提供依据.该模型考虑了影响信任的主观因素和客观因素,且具有统计基础,为解决开放分布系统的安全问题提供了新方法.     

基于时间窗反馈机制的动态P2P信任模型

现有的信任模型在识别不诚实反馈、惩罚恶意节点的策略性动态改变行为和激励机制等方面存在不足.为了解决这些问题,提出了一个基于时间窗的局部信任模型TW-Trust,统一考虑了信任、信誉与激励三者的内在关系,同时考虑了时间因素对信任计算的影响.使用直接信任、历史信誉、差别信誉、滥用信誉、相似度和激励效用6个参数来计算网络节点信任度,并通过反馈控制机制动态调节这些参数,提高了信任模型的动态适应能力.仿真实验结果表明,TW-Trust较已有模型能更好地处理恶意节点策略性动态变化行为和不诚实反馈对系统的攻击,能更好解决相似度计算中的稀疏性问题,在信任计算误差和事务失败率等性能指标上有较大提高.     

P2P网络中基于模糊推理的信任综合评?判

针对P2P网络环境下实体之间的信任关系存在模糊性的特点,以模糊推理理论为基础,给出了一种基于信任的模糊命题演算的演绎系统,建立了P2P网络环境下的一种基于三Ⅰ模糊推理算法的信任推荐和信任评价模型.模型中,对基于信任的多条推荐规则进行了模糊推理,达到对推荐信任的综合评价,从而建立起了一种新的P2P网络环境下信任模型.仿真实验结果表明,该信任模犁能够较大程度地提高交易的成功率,可以使得节点之间更加有效地建立信任关系.     

面向P2P网络应用的基于声誉的trust管理模型

提出一种新的面向P2P网络应用的信任管理模型,该模型根据节点的历史交易情况和系统中其他节点的推荐计算节点的信任度,给出了普遍适用的、具体量化和更新推荐者可信度的数学表达形式,同时考虑到节点的自主性动态行为影响信任度计算的不确定性,引入风险因素,提出了采用信息熵理论来量化风险,将实体之间的信任程度和信任的不确定性统一起来.仿真实验及分析表明,该信任模型能够有效识别恶意节点,相比已有的一些信任模型在系统成功交易率、模型的安全性等问题上有较大改进,可以使节点之间更有效的建立信任关系.     

Novel approach of distributed & adaptive trust metrics for MANET

It is known to all that mobile ad hoc network (MANET) is more vulnerable to all sorts of malicious attacks which affects the reliability of data transmission because the network has the characteristics of wireless, multi-hop, etc. We put forward novel approach of distributed & adaptive trust metrics for MANET in this paper. Firstly, the method calculates the communication trust by using the number of data packets between nodes, and predicts the trust based on the trend of this value, and calculates the comprehensive trust by considering the history trust with the predict value; then calculates the energy trust based on the residual energy of nodes and the direct trust based on the communication trust and energy trust. Secondly, the method calculates the recommendation trust based on the recommendation reliability and the recommendation familiarity; adopts the adaptive weighting, and calculates the integrate direct trust by considering the direct trust with recommendation trust. Thirdly, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. The feature of the proposed method is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation experiments and tests of the practical applications of MANET show that the proposed approach can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

     

基于上下文因素的P2P动态信任模型

构建一个基于上下文因素的多维度P2P信任模型,结合考虑时间衰减、交互重要性和交互次数度量实体交互信任,基于Dice相似度给出信任相似度算法,设计一种多链路反馈可信度融合算法,聚合直接交互、评价相似度和信任链传递计算实体的推荐信任,综合直接信任和推荐信任进行实体信任的评估,并提出一种新的信任更新和奖惩机制。实例分析表明,模型较好地体现了上下文因素对信任计算的影响,增强了模型在上下文因素的敏感性。     

Ad Hoc网络中一种基于救赎机制的信任模型

在Ad Hoc网络中,节点由于自私等恶意原因以及链路错误导致拒绝转发数据,需要采用适当的机制来提高网络的转发性能。提出一种Ad Hoc网络中的信任模型来激励节点间的协作,在此信任模型中,每个节点通过直接监控和其它节点的推荐监控信息共同完成对邻居节点的信任评价,并维护所有邻居节点的信任值,同时在信任值更新和重建中引入信任值衰退和信任救赎机制来体现模型的健壮性。实验表明该信任模型中的信任救赎机制能提高网络可用性,且该模型有效抑制了恶意节点的行为。     

一种基于云模型的WSNs节点信誉安全方案

针对已有基于轻量云模型的节点信誉安全方案中存在的决策困难问题以及推荐节点的恶意行为识别问题,提出了优化的轻量云模型MLCM(Modified Light-weighted Cloud Model),并在此基础上设计实现一种新型的节点信誉安全方案.首先,在传统无线传感网信任管理信任值的处理方式的基础上,对节点的直接信任值和间接信任值进行综合处理后再利用云模型简化逆向云算子进行计算,以解决信任误判问题;其次,用云隶属度函数计算推荐信任值,在涉及推荐节点信任值计算时可以提高恶意节点识别的准确度.实验表明,该方案在克服传统的入侵容忍和敏感度之间矛盾问题的同时,还解决了攻击节点对单一节点发动攻击时造成的决策困难问题和恶意节点准确识别问题.     

MANET 中基于信任的安全位置辅助路由模型

In view of the security weakness in resisting the active attacks by malicious nodes in mobile ad hoc networks, the trust metric is introduced to defend those attacks by loading a trust model on the previously proposed Distance Based LAR. The improved Secure Trust based Location Aided Routing algorithm utilizes direct trust and recommendation trust to prevent malicious nodes with low trust values from joining the forwarding. Simulation results reveal that ST LAR can resist attacks by malicious nodes effectively; furthermore, it also achieves better performance than DBLAR in terms of average end to end delay, packet delivery success ratio and throughput.     

Trust evaluation method for clustered wireless sensor networks based on cloud model

Trust Management has been proved to be an effective method to detect malicious nodes and ensure security in Wireless Sensor Networks (WSNs). While, most existing trust management methods are not good at dealing with uncertainty of trust relationship such as randomness, fuzziness in WSNs, which leads to inaccurate trust metric. In this paper, a trust evaluation method for clustered wireless sensor networks based on cloud model is proposed and evaluated, which implements the conversion between qualitative and quantitative of sensor nodes’ trust metrics in order to achieve better trust evaluation. Firstly, the method considers multi-factors including communication factor, message factor and energy factor and builds mathematical model for each trust factor to get factor trust cloud. Secondly, immediate trust cloud is calculated by assigning adjustive weights for each factor trust cloud and combining them. Thirdly, recommendation trust cloud and immediate trust cloud are synthesized according to time sensitive factor in order to get final trust cloud. Furthermore, the final trust cloud of sensor node is converted to trust grade by trust cloud decision-making. Verification Experiments manifest that the proposed method has feasibility and accuracy in the aspect of evaluating sensor nodes’ trust. Moreover, comparison experiments under different attacks show that our method is sensitive to multiple attacks, it outperforms other trust evaluation methods not only in the accuracy of detecting malicious nodes, but also in the tolerance of abnormal conditions.     

An Effective Model for Indirect Trust Computation in Pervasive Computing Environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.