P2P网络环境下基于推荐的信任模型

基于网络中节点之间不仅仅局限于直接交易建立起来的信任关系,还包括了第三方推荐信任的事实,提出了在P2P网络环境下基于推荐的信任模型。该模型用成功次数与失败次数在总交易数目中的比例作为直接信任度,将交易信誉与推荐信誉明确区分出来,引入了偏移因子计算推荐节点的可信性,通过惩罚因子和风险因素动态平衡节点直接信任度和其他节点的推荐信任度,得到目标节点的综合信任值,并给出仿真实验验证。实验结果证明,模型计算的综合信任值更趋近其真实值,并且能抵抗恶意节点的诋毁、协同作弊等威胁。     

P2P环境下的一种混合式信任模型

提出了一种P2P环境下的混合式动态信任模型来解决当前P2P网络的安全性差、难于管理等问题缺陷。该模型融合了本地信任、推荐信任和全局信任模型,通过有机的结合能充分发挥各自模型的优点。同时通过相应的反馈机制能够有效地判断节点信任度的变化和抵御诋毁、夸大等安全问题。仿真结果表明,该模型能有效地判断节点的信任度,同时具有良好的安全性。     

一种基于对等组的新型P2P敏捷信任模型

针对P2P网络的安全需求，提出了一种新型P2P敏捷信任模型。该模型建立在半分布式拓扑结构上，安全对等组内、组间的信任值计算具有良好的敏捷性。对于模型中的信任度求解、节点成员表的历史记录维护、冒名、诋毁及协同作弊等关键问题给出了解决策略。设计并实现的原型系统进一步验证了模型可行性。     

P2P系统的信任研究

由于P2P系统的开放、匿名等特点,使得P2P系统对节点缺乏约束机制,节点间缺乏信任。针对以上问题,本文提出了一种新的P2P系统信任模型,该模型根据系统中节点的历史交易情况和系统中其它节点的推荐计算节点的信任度,节点根据计算的结果决定是否进行交易。仿真试验及分析表明,该模型能有效地评估节点的信任度,隔离恶意节点,提高下载成功率。     

P2P网络中基于激励机制的信任模型

针对P2P网络提出一种信任模型。通过区分直接信任和间接信任,使推荐信任度主要依赖节点以往的成功推荐次数而非直接信任度,以抵御拥有较高直接信任度的节点对正常节点进行诋毁或欺骗。为避免非恶意节点因为网络延时等原因导致服务失败而被孤立出网络,引入激励机制和信任重建机制,定期提高非恶意节点的信任值,使其能重新加入网络,从而加强网络的容错性。实验结果表明,该模型能有效保护非恶意节点,孤立恶意节点,使网络具有更好的健壮性。     

构造P2P环境下混合式信任模型

针对P2P网络中存在的安全性和可管理性较差、现有信任模型中信任度计算复杂等问题,提出了一种P2P环境下混合式信任模型。该模型采用混合式P2P网络结构,利用时间段机制计算节点信任度,有效地预防节点间的“夸大”、“诋毁”等行为;提出了模型的构建方法以及交互评价反馈方式。仿真结果表明该模型运算量小,并能够有效地孤立和识别恶意节点,具有较强的安全性能。     

P2P网络环境下信任模型的研究

由于对等(P2P)网络的开放性和匿名性,各种恶意节点的恶意行为层出不穷,严重影响了网络的正常运行,加之传统的信任管理模型并不能很好地适应对等网络环境,提出了一种基于分级推荐的P2P网络信任模型(GRTM)。仿真实验表明,基于分级推荐的信任模型能有效评估节点的信任度,交易成功率优于传统的信任管理模型。     

一种基于主观逻辑理论的P2P网络信任模型

由于P2P系统的开放、匿名等特点，使得P2P系统对节点缺乏约束机制，节点间缺乏信任。针对以上问题本文提出了一种基于主观逻辑理论的P2P网络信任模型，并在信任的计算中引入风险的机制，有效防止协同作弊和诋毁的安全隐患。实验和分析结果表明，这种信任模型能更加精确地评估节点的信任度，从而能更加有效地解决P2P网络环境中存在的安全问题。     

一种综合的P2P网络信任模型

安全有效的信任模型是保证P2P 系统高效、稳定的关键技术之一。介绍一种适用于P2P 网络的综合信任模型,它参考了社会网络中信任关系的建立方法,从两个角度来计算节点的综合可信度。该模型中引入了非对称加密等安全机制,通过分析与仿真证明该模型能有效地抑制冒名和诋毁等非法行为,具有较强的安全性。     

P2P系统中基于声誉的信任评估机制

P2P系统能够使陌生节点之间进行在线交易,但安全问题越来越受到人们的关注。因此,如何在P2P系统中构建一个有效的信任机制来帮助在节点之间建立信任是目前P2P技术研究的一个热点。提出了一种新的P2P系统中基于声誉的信任评估机制,该机制较全面地考虑了影响信任度的因素,改进了局部声誉与全局声誉的计算方法,降低了信任度的计算负载,并且引入了黑名单机制。实验结果表明,该机制能有效地评估节点的信任度,识别和隔离恶意节点,提高系统交易成功率,并能有效地应用于P2P系统中。     

P2P网络下基于推荐的信任模型

基于推荐信任机制,引入正态概率密度函数的概念,对信任度进行描述。提出一种对信任度进行概率分析的评估方法,可动态地适应用户的安全需求,减弱在多路径推荐中由于恶意实体推荐所带来的负面影响,提高信任计算结果的稳定性。分析和模拟试验表明该模型的必要性和有效性,可以更好地解决P2P网络带来的安全问题。     

一种细粒度的基于灰色关联度的P2P信任模型①

已有的P2P网络信任模型过于粗糙,对反馈评价进行综合的能力不足。针对这一问题,提出了一种细粒度的基于灰色关联度的P2P信任模型GM—TRUST,根据节点的兴趣和专长将节点化分为不同的域,通过对具体服务各属性评价的综合得出直接信任。引入记忆因子来刻画信任随时间衰减的特性,并利用基于灰色相关度的方法来量化推荐信任的准确度。分析与实验均表明本模型与以往的信任模型相比,能够更准确地评估出节点的信任值,对动态恶意节点和不诚实反馈节点的攻击具有很好的抑制能力。     

Trustworthy-based efficient data broadcast model for P2P interaction in resource-constrained wireless environments

In a decentralised system like P2P where each individual peers are considerably autonomous, the notion of mutual trust between peers is critical. In addition, when the environment is subject to inherent resource constraints, any efficiency efforts are essentially needed. In light of these two issues, we propose a novel trustworthy-based efficient broadcast scheme in a resource-constrained P2P environment. The trustworthiness is associated with the peer?s reputation. A peer holds a personalised view of reputation towards other peers in four categories namely SpEed, Correctness, qUality, and Risk-freE (SeCuRE). The value of each category constitutes a fraction of the reliability of individual peer. Another factor that contributes to the reliability of a peer is the peer?s credibility concerning trustworthiness in providing recommendation about other peers. Our trust management scheme is applied in conjunction with our trust model in order to detect malicious and collaborative-based malicious peers. Knowledge of trustworthiness among peers is used in our proposed broadcast model named trustworthy-based estafet multi-point relays (TEMPR). This model is designed to minimise the communication overhead between peers while considering the trustworthiness of the peers such that only trustworthy peer may relay messages to other peers. With our approach, each peer is able to disseminate messages in the most efficient and reliable manner.     

An incentive compatible reputation mechanism for P2P systems

In peer-to-peer (P2P) systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of P2P systems. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. This paper presents an incentive compatible reputation mechanism for P2P systems. It has two unique features: (1) a recommender’s trustworthiness and level of confidence about the recommendation is considered for a more accurate calculation of reputations and fair evaluation of recommendations. (2) Incentive for participation and honest recommendation is implemented through a fair differential service mechanism. It relies on peer’s level of participation and on the recommendation credibility. Theoretic analysis and simulation show that the reputation mechanism we propose can help peers effectively detect dishonest recommendations in a variety of scenarios where more complex malicious strategies are introduced. Moreover, it can also stimulate peers to send sufficiently honest recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions.     

PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities

Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to minimize threats in such communities is to use community-based reputations to help estimate the trustworthiness of peers. We present PeerTrust - a reputation-based trust supporting framework, which includes a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system, and a decentralized implementation of such a model over a structured P2P network. PeerTrust model has two main features. First, we introduce three basic trust parameters and two adaptive factors in computing trustworthiness of peers, namely, feedback a peer receives from other peers, the total number of transactions a peer performs, the credibility of the feedback sources, transaction context factor, and the community context factor. Second, we define a general trust metric to combine these parameters. Other contributions of the paper include strategies used for implementing the trust model in a decentralized P2P environment, evaluation mechanisms to validate the effectiveness and cost of PeerTrust model, and a set of experiments that show the feasibility and benefit of our approach.     

一种时域上的P2P信任模型

传统的P2P信任是根据节点间交易成功和失败的次数来合成的,该值是个累积量,而实际的信任值是时变量。提出了一种新的P2P时域信任模型,对于局部信任,通过对每个时间段合成一个信任,然后根据时间段的新近赋予不同的加权合成局部信任;对于全局信任来说,随着时间的推移,发起节点会越来越重视自身对目标节点的信任评价,而其它节点的推荐值会得以削弱。该模型刻画了信任的动态性,能够有效地防止不良节点的信任短期积聚以及依靠其它节点共谋等恶意行为。     

一种基于概率统计方法的P2P系统信任评价模型

现有的P2P系统信任评价模型正面临着两种恶意节点的攻击行为--策略性欺骗和不诚实推荐,严重影响了模型计算节点信任评价的准确性和有效性.针对现有模型存在的不足,提出了一种基于概率统计方法的信任评价模型.该模型借鉴人类社会中主观信任关系的概念,依据直接经验和反馈信息,利用概率统计方法分别计算节点的直接信任和推荐信任,并通过区分直接经验的重要程度,区分反馈信息及其推荐者的可信度,提高信任评价模型的有效性.仿真实验分析说明,与已有的信任评价模型相比,该模型能够更有效地抑制策略性欺骗和不诚实推荐的威胁,特别是复杂的协同作弊方式对系统的攻击.     

Peer profile based trust model for P2P systems using genetic algorithm

The open and anonymous nature of P2P allows peers to easily share their data and other resources among multiple peers, but the absence of a defensible border raise serious security concerns for the users. There is a lack of accountability for the content that is shared by peers and it is hard to distinguish malicious users from honest peers. Establishing Trust relationship between peers can serve as the metric to determine the veracity of the shared content and reliability of the peers. Most of the research work in this area is on Reputation based trust management where trust is determined on the basis of recommendation of other peers. Such recommendations are subjective and can be biased. A number of peers can also collude to provide false testimony for malicious peers. This paper proposes a novel Trust model that combines peer profiling with anomaly detection technique. Each peer can establish trust based on its own prior activities with other peers by comparing the current activity of a peer with its historical data and Genetic Algorithm (GA) has been employed to detect the anomalous behavior. Peer profile is updated dynamically with every transaction using GA operator’s crossover and mutation. This model has been tested using a file sharing application against common attacks and the results obtained are compared with statistical anomaly detection approach.     

GenTrust: A genetic trust management model for peer-to-peer systems

In recent years, peer-to-peer systems have attracted significant interest by offering diverse and easily accessible sharing environments to users. However, this flexibility of P2P systems introduces security vulnerabilities. Peers often interact with unknown or unfamiliar peers and become vulnerable to a wide variety of attacks. Therefore, having a robust trust management model is critical for such open environments in order to exclude unreliable peers from the system. In this study, a new trust model for peer-to-peer networks called GenTrust is proposed. GenTrust has evolved by using genetic programming. In this model, a peer calculates the trustworthiness of another peer based on the features extracted from past interactions and the recommendations. Since the proposed model does not rely on any central authority or global trust values, it suits the decentralized nature of P2P networks. Moreover, the experimental results show that the model is very effective against various attackers, namely individual, collaborative, and pseudospoofing attackers. An analysis on features is also carried out in order to explore their effects on the results. This is the first study which investigates the use of genetic programming on trust management.