P2P文件共享系统中的分组信誉驱动机制

在P2P文件共享系统中,常会出现许多有策略的欺骗行为,而现有的信任模型并不能完全消除交易的风险。综合局部信任机制和全局信任机制,根据节点间相关的共享记录数据提出一种新的信誉计算方法,并进一步提出受信誉驱动的分组组织管理和节点搜索算法。仿真结果证明该信誉驱动机制能搜索到信誉值高的节点作为交易对象,并能有效降低系统面对合谋恶意节点和具有交易策略的恶意节点攻击时的交易风险。     

Behavior-based reputation management in P2P file-sharing networks

Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior.     

一种节点信誉相关的P2P网络信任管理模型

现有的P2P系统中存在大量的欺诈行为和不可靠的服务.本文通过模拟社会关系网络中信任的形成机制,提出P2P网络信任管理模型TMMRN,TMMRN通过考察节点的信誉值来进行安全交易,节点的信誉主要来自于其他节点对它的加权信任反馈,在信誉计算中增加了激励机制.TMMRN还可减少交易时的网络负担.实验表明TMMRN可提高信誉值的计算效率,能有效抵抗恶意节点的攻击,还可激励懒惰节点主动参与到系统中来.     

H-Trust: A Group Trust Management System for Peer-to-Peer Desktop Grid

Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated. To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations. Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems even when a large portion of peers are malicious.     

A GroupTrust model based on service similarity evaluation in P2P networks

The open and anonymous nature of peer‐to‐peer (P2P) networks makes it an ideal medium for attackers to spread malicious contents, which in turn leads to lower quality of network services due to lack of effective trust management mechanism. To improve the quality of services (or transactions), this paper proposes a novel trust and reputation model, named as GroupTrust, based on peer group and evaluation similarity degree in P2P networks. In the proposed model, trust relationships between peers are divided into three categories: trust relationship within a peer group, trust relationship between different groups, and trust relationship between a peer in a peer group with another peer out of this peer group. The model presents the evaluation similarity degree under different context of services and gives local and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks efficiently by comparison with existing models. © 2010 Wiley Periodicals, Inc.     

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.     

一种基于差评散布的P2P网络信任机制

面对各种网络攻击,P2P网络需要有效的信任机制隔离恶意节点,保证节点的成功交易。考虑节点行为特征和差评的重要性,提出基于差评散布的信任机制。服务节点一旦提供的服务被给出差评,对其近期交易的相关节点进行差评的散布,加大差评对服务节点声誉的影响力度。经过二次计算的节点声誉值能真实反映节点近期的声誉水平与交易趋势。实验结果表明,该信任机制能保证正常节点的交易成功率,有效对抗各种攻击行为。     

一种激励相容的P2P信誉模型

针对现有的信任模型不能很好地处理P2P网络环境中恶意节点提供虚假服务的欺作行为,及不积极提供诚实推荐的问题,提出了一种激励相容的P2P信誉模型(简称ICRM)。该模型使用时间区间的概念来标示经验和推荐的时间特性,利用直接信任度、推荐信任度及推荐可信度等机制来精确描述节点的实际信任等级,并引入参与层次来度量节点提供推荐的积极程度,从而有效地识别与抑制不同类型的恶意节点,激励节点积极提供诚实推荐。仿真实验表明,ICRM能够有效地抑制恶意节点的欺作行为及不诚实反馈行为,并能有效解决节点推荐积极性不高的问题。     

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.     

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.     

安全结构化对等网络的节点信誉管理机制

提出一种安全结构化对等网络的节点信誉管理机制。使用全局储存方式保存信誉度信息,将文件信誉与节点信誉相结合,避免恶意节点通过修改标识符伪装友好节点的行为。对文件共享系统中的基本操作过程进行分析。在各种节点行为下进行模拟实验,结果证明了该信誉管理机制的有效性。     

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.     

Combining search and trust models in unstructured peer-to-peer networks

Effectiveness of Peer-to-Peer (P2P) systems highly depends on efficiency and scalability of their search algorithms. Moreover, managing trust is a key issue for wide acceptance of P2P computing. Surprisingly, the majority of the available trust systems ignore the underlying search algorithm and assume it is preexisting. We claim that combining search and trust systems yields significant performance gains in terms of network traffic and query success rate. In this paper, we propose a robust and efficient trust based search framework for unstructured P2P networks. Our framework maintains limited size routing indexes combining search and trust data to guide queries to most reputable nodes. By dynamically selecting reputable nodes as score managers, our scheme tracks the reputation of participating peers. In an alternative approach, we aggregate partial reputation values obtained from reverse query paths to introduce a low overhead method for estimating reputation scores of peers. Through P2P network simulation experiments, we find significant performance gains in using our framework.     

一种考虑节点角色的P2P信任模型

在对等网络中,如何对节点行为进行准确的评估是信任模型研究的重点.经过学习和分析,发现现有的模型几乎都是基于节点作为被推荐者这一角色,而忽略了自身的推荐角色.对此本文提出两重推荐信任模型,从节点的两重角色考虑,为每个节点设计两重评估标准.若干次交易后,该模型能够使正常节点形成紧密的交易关系,有效地遏制了恶意节点的交易行为.实验表明该模型在面对夸大、合谋、伪装和单一行为威胁时较其他一些模型更具有效性.     

一个非结构化P2P网络中基于信誉的激励机制

在P2P网络中,freerider节点和恶意节点的不合作行为严重影响了P2P服务的可用性。为此,本文提出了一个应用于非结构化P2P环境下基于信誉的激励机制。该机制通过有限的信任信息的共享,实现信任评估和推荐信任度评估;并以此为基础,通过有效的拓扑构造和服务选择策略及服务请求冲突解析策略的选择,实现对合作节点的激励和对freerider和恶意节点的遏制及惩罚。分析及仿真实验表明,本文提出的基于信誉的激励机制是简单有效的。     

一种基于反馈可信度的分布式P2P信任模型

开放、共享与匿名的Peer-to-Peer(简称P2P)网络已经取得了越来越多的应用,无中心对等的特性也吸引了越来越多的用户.但由于其网络中的节点不受约束,资源的共享是用户自愿的行为,因此节点间的信任很难通过传统的信任机制建立.一种可行的解决方案是借鉴人际网络中的信任关系,建立一种基于信誉的全局信任模型.已有的工作基本建立在信任度高的节点其反馈也更可信这个假设的基础上,将节点的反馈质量简单地等同于服务质量.针对这一问题,提出了一种基于节点反馈可信度的分布式P2P全局信任模型(简称FCTrust),用于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.分析及仿真实验结果表明,FCTrust较已有的全局信任模型在遏制更广泛类型的恶意节点攻击的有效性、迭代计算的收敛性及消息成本上有较大提高.     

Multifactor hierarchical fuzzy trust evaluation on peer-to-peer networks

Decentralized Peer-to-Peer (P2P) networks offer not only opportunities but also threats. Due to the autonomy, self-interest and heterogeneousness of peers, the interaction outcomes are uncertain. One way to minimize the threats in such an open environment is exploiting the reputation method to evaluate the trustworthiness and predict the future behaviors of peers. While most of the existing reputation-based trust models focus on preventing network from the malicious peers, peers’ capabilities to fulfill the tasks are mostly ignored. In this paper, we present a novel trust model MHFTrust which quantifies and compares the trustworthiness of peers based on hierarchical fuzzy system. Six capability factors are identified to describe the peers’ trust on the capability, and one security factor, named “Malicious behavior” is used to evaluate the peers’ trust on security. Our trust model consisted of local-trust computation based on fuzzy techniques and global reputation aggregation, which integrates feedback from other peers to produce a global reputation for each peer. Credibility and weight of feedback are introduced to facilitate the computation of global reputation. It is shown in simulation that our trust model greatly improves the efficiency of P2P system, while the number of inauthentic files on the network is significantly decreased.     

Securing decentralized reputation management using TrustGuard

Reputation systems have been popular in estimating the trustworthiness and predicting the future behavior of nodes in a large-scale distributed system where nodes may transact with one another without prior knowledge or experience. One of the fundamental challenges in distributed reputation management is to understand vulnerabilities and develop mechanisms that can minimize the potential damages to a system by malicious nodes. In this paper, we identify three vulnerabilities that are detrimental to decentralized reputation management and propose TrustGuard—a safeguard framework for providing a highly dependable and yet efficient reputation system. First, we provide a dependable trust model and a set of formal methods to handle strategic malicious nodes that continuously change their behavior to gain unfair advantages in the system. Second, a transaction-based reputation system must cope with the vulnerability that malicious nodes may misuse the system by flooding feedbacks with fake transactions. Third, but not the least, we identify the importance of filtering out dishonest feedbacks when computing reputation-based trust of a node, including the feedbacks filed by malicious nodes through collusion. Our experiments show that, comparing with existing reputation systems, our framework is highly dependable and effective in countering malicious nodes regarding strategic oscillating behavior, flooding malevolent feedbacks with fake transactions, and dishonest feedbacks.     

采用排名反馈的P2P名誉评价模型

P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相...     

基于交易审计的对等网络信用评估算法

free-riding现象以及各种恶意节点行为是制约对等网络应用发展的重要因素。以节点的交易审计为基础,结合信任评估模型,提出一种针对节点交易记录的信用评估算法。利用激励机制抑制网络中的free-riding现象,采用签名和审计方式防止各种安全性攻击。仿真实验结果表明,该算法具有较好的性能和实用性。