Malicious Peers Research Articles

Impact of Feedback on Trust in P2P Networks

Feedback plays an important role in P2P trust systems. Existing trust systems usually assume that most of normal peers have idealized feedback behaviors, that is, peers can provide honest feedback after download immediately. However, studies show that feedback sparsity and delay widely exist in a real P2P network. Unfortunately, their effect on trust systems has received little attention in previous researches. In this paper, we propose a feedback model considering feedback ratio and feedback delay to analyze the effect. We find that most trust systems have poor reliability when the number of feedbacks is small in a decentralized P2P network. We propose an implicit feedback mechanism based on the retention time of files to remove the impact of user feedback on trust systems. Simulation results show that trust systems using implicit feedback can not only effectively isolate normal peers from malicious peers but also provide differential services for normal peers with different behaviors.

A Simulation Tool for Real-Time Hybrid-Cooperative Positioning Algorithms

The authors propose a simulation tool (ST) able to test real-time hybrid GNSS/terrestrial and cooperative positioning algorithms that fuse both pseudorange measurements from satellites and terrestrial range measurements based on radio frequency communication performed between nodes of a wireless network. In particular, the ST simulates devices belonging to a peer-to-peer (P2P) wireless network where peers, equipped also with a GNSS receiver, cooperate among them by exchanging aiding data in order to improve both positioning accuracy and availability. Furthermore, the authors propose a method to increase the robustness of cooperative algorithms based on the estimated position covariance matrix. In particular, the proposed approach assures a faster estimation convergence and improved accuracy while lowering computational complexity and network traffic. Finally, the authors tested the sensitivity of the implemented positioning algorithms through the ST in two different scenarios, first in presence of high level of pseudorange noise and then in presence of a malicious peer in the P2P network.

@Trust: A trust model based on feedback-arbitration in structured P2P network

Aiming at shortcomings in feedback information aggregation and trust decision in existing trust models, a novel trust model, named @Trust, is proposed for structured P2P networks. @Trust calculates the node’s credibility of service, feedback and arbitration based on the evaluations of the specific service which were arbitrated according to the rule of majority in human society, to enhance the model against the malicious peers. @Trust also involves the applying of the credibility of specific services and the punishment mechanism to improve the trust decision and to resist the repeated attacks of the malicious peers. The simulation results show that @Trust has advantages in distinguishing out the false feedback and resisting the malicious peers, and thus improves the availability of P2P networks.

A unified trust management strategy for content sharing in Peer-to-Peer networks

Content sharing has emerged as a popular application in Peer-to-Peer (P2P) networks. The increasing number of free-riders and untrustworthy peers in content sharing network reduces the credibility of the shared contents. Also, the malicious peers pose the danger of security violation and bandwidth wastage by sharing polluted or infected contents in the network for their personal benefits. In this paper, we propose a unified trust management strategy by combining trust and security models with reputation based preferences for efficient content sharing in P2P networks. The problems in P2P content sharing, like free-riding, content pollution, content poisoning, peer availability, content availability, DoS attack, and flash crowd are considered in our unified trust management model. The model provides controlled scalability of the overlay network, and concurrently ensures the availability of contents for longer time in the network. The provider initiating content sharing is protected from Denial-of-Service (DoS) attack through restricted network size and interspersing the provider within a set of distributed peers. Performance measures, such as scalability, content availability, flash crowd and prevention of polluted or infected content distribution are compared with BitTorrent in heterogeneous environment. The advantage of this model is that it can be deployed easily in BitTorrent-like P2P networks. Simulation results show that controlled scalability helps in efficient utilization of bandwidth and protection for the providers, ultimately ensuring trust on the P2P network and the content shared.

Peer-to-peer indirect reciprocity via personal currency

Motivating peers to contribute services is critical to the success of peer-to-peer (P2P) systems. Incentive protocols use reciprocity to enforce contributions. Indirect reciprocity schemes are more efficient than direct reciprocity schemes for large-scale P2P systems under high churn rate. In this paper, we propose an indirect reciprocity scheme, called FairTrade, in which peers issue personal currencies to trade services in a P2P system. Personal currency enables indirect reciprocity without relying on any central banks or authorities. It wins extra robustness over global currency as well as much improved trading flexibility and efficiency over direct reciprocity schemes. The acceptance degree of a personal currency depends on the issuer’s service capability and reliance. Peer credit limit is introduced to represent the amount of personal currency that will be accepted by other peers. Every peer as a creditor applies a Bayesian network model to setting peer credit limit for a trading partner peer as a creditee. The Bayesian network model learns the creditee’s capability and reliability and anticipates the associated profits and risks for credit setting. Using simulations on a file-sharing P2P system, we demonstrate that FairTrade achieves 100% success rate of download requests without malicious peers, and maintains over 90% success rate even with 50% malicious nodes. The system warms up quickly and does not assume any altruistic service or other kind of help. On average, the system traffic stabilizes before peers issue their second download requests. All these good performances are achieved with extremely low trading overhead, which takes up less than 1% of the total traffic.

Secure Communication in Unstructured P2P Networks based on Reputation Management and Self Certification

In unstructured P2P networks there is a possible of malicious codes and false transactions. It generates the false identities in order to perform false transactions with other identities. The proposed method uses the concept of DHT and reputation management which provides efficient file searching. The self certification (RSA ALGORITHM and MD5) is used for ensuring secure and timely availability of the reputation data of a peer to other peers. The reputations of the peers are used to determine whether a peer is a malicious peer or a good peer. Once the malicious peer is detected the transaction is aborted. The reputation of a given peer is attached to its identity. The identity certificates are generated using self-certification, and all peers maintains their own (and hence trusted) certificate authority which issues the identity certificate(s) and digital signature to the peer References

An economic perspective of message-dropping attacks in peer-to-peer overlays

Peer-to-peer networks have many advantageous security properties, including decentralization, natural load-balancing, and data replication. However, one disadvantage of decentralization is its exclusion of any central authority who can detect and evict malicious peers from the network. It is therefore relatively easy to sustain distributed denial-of-service attacks against these networks; malicious peers simply join the network and fail to forward messages. This article shows that peer-to-peer message-dropping attacks can be understood in terms of a well-established category of economic theory: the theory of the second best. In particular, peers who wish to continue service during an attack seek a second best solution to a utility optimization problem. This insight reveals useful connections between economic literature on the second best and computer science literature on peer-to-peer security. To illustrate, we derive and test an economics-inspired modification to the Chord peer-to-peer routing protocol that improves network reliability during message-dropping attacks. Under simulation, networks using the modified protocol achieve a 50% increase in message deliveries for certain realistic attack scenarios.

Peer-to-Peer multimedia sharing based on social norms

Designing incentive schemes for Peer-to-Peer (P2P) multimedia sharing applications, where the participating peers find it in their self-interest to contribute resources rather than to “free-ride”, is challenging due to the unique features exhibited by such networks: large populations of anonymous peers interacting infrequently, asymmetric interests of peers, network errors, multiple concurrent transactions, low-cost implementation requirements, etc. In this paper, to address these challenges, we design and rigorously analyze a new family of incentive protocols that utilizes social norms. In the proposed protocols, each peer maintains a reputation reflecting its past behaviors in the P2P system (i.e. whether the peers have followed or not the social strategy prescribed by the social norm), and the social norm rewards and punishes peers depending on their reputations. We first define the concept of a sustainable social norm, under which no peer has an incentive to deviate from the social strategy prescribed by the protocol. We then formulate the problem of designing optimal social norms, which selects the social norm that maximizes the network performance among all sustainable social norms. In particular, we prove that, given the P2P network and peers' characteristics, social norms can be designed such that it becomes in the self-interest of peers to contribute their contents to the network rather than to free-ride. We also investigate the impact of various punishment schemes on the social welfare as well as how should the optimal social norms be designed if altruistic and malicious peers are active in the network. Our results show that optimal social norms are capable of deterring free-riding behaviors and providing significant improvements in the sharing efficiency of multimedia P2P networks.

METrust: A mutual evaluation-based trust model for P2P networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer (P2P) network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers' evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers' malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers' own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

Statistical Mechanisms for Detecting Malicious Behaviors in Resource Allocation from Non-cooperative P2P Environments

Abstract A Peer-to-Peer (P2P) system relies on the cooperation of the peers and the contributions of their resources. To motivate autonomous peers share their resources, the system needs to support effective resource allocation strategies with respect to peers’ task priorities, and their personal information about valuation. However, peers may tend to be selfish for saving their limited resources and act as the free-riders. Some peers may even be malicious with the goal to do damage to the system. In this paper, we present a bidding based approach for resource allocation to address these issues. We investigate peers’ bidding strategies under different scenarios in terms of probability distributions that peers’ valuations of their prioritized tasks follow in achieving the Nash equilibrium. For resisting the damage to the P2P system brought by malicious peers, we explore different types of malicious behavior and present several statistical mechanisms to detect the malicious peers. The algorithm is also pres...

A Trust and Reputation Model Considering Overall Peer Consulting Distribution

In new widespread peer-to-peer (P2P) systems, peers are exposed to great risk due to frequent trading with unfamiliar peers. Therefore, trust and reputation mechanisms become important issues. For computational efficiency, this paper focuses on localized information trust/reputation mechanisms. Previous studies have not paid much attention to the overall distribution of peer interactions. Based on the scale-free feature of real-world networks, we introduce a power-law distribution of the number of neighbors in P2P trust and reputation systems. To rigorously distinguish the effects of the overall considerations introduced herein, we compare the model proposed in this paper with models in previous studies under the same set of parameters. Simulation results show that the proposed model can discern a small difference between real quality of service (QoS) and other peers' feedback while distinguishing the malicious peers, even when the exaggeration coefficient is high. When one or a group of peers change their QoS, the model exhibits a quick reaction to this change. This response is demonstrated by a rapid decrease in reliability when the QoS change is downward and a slow increase when the change is upward. A slow reaction to the upward QoS change may exclude those peers who frequently change their QoS and encourage consistent reliable service providers.

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.

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 peerʼs 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 peerʼs 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.

A mechanism based on reputation in P2P networks to counter malicious packet dropping

In P2P (Peer-to-Peer) networks, some malicious peers can impact on overall networks performance. One of the malicious behaviors of these peers is malicious packet dropping. In this paper, our focus is to detect and to exclude peers that misbehave by dropping some or all packets. Here, we propose a reputation-based mechanism for solving the problem efficiently. The proposed mechanism uses both direct reputation information and indirect reputation information to compute comprehensive reputation of a peer. At the same time, history reputation information is also taken into account to provide faults tolerance capability and we regulate the imprecision based on the fact that the cause of packet dropping can be complex. Finally, the peers with bad comprehensive reputation can be detected easily and then will be excluded from the network. In this way, our proposed mechanism improves the performance of P2P networks without increasing computational overhead.

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.

DHTrust: a robust and distributed reputation system for trusted peer‐to‐peer networks

SUMMARYThe anonymity and dynamic character of a Peer‐to‐Peer (P2P) network makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT (Distributed Hash Table) trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the DHT to distribute local reputation to trade‐off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in the P2P network, i.e. providing service and issuing reputation scores. To adapt to the dynamic P2P networks, we take dynamic node mechanism into account. Our scheme can assure convergence effectiveness and robustness, when nodes enter or leave the system. We conduct extensive simulations to evaluate the performance of DHTrust. The results show that our system makes significant improvement in convergence speed and aggregation accuracy. Moreover, it is robust to malicious peers. Copyright © 2011 John Wiley & Sons, Ltd.

An Incentive Compatible Reputation Model for P2P Networks

An important challenge regarding peer’s trust assessment in peer-to-peer (P2P) networks is how to cope with such issues as the fraudulent behaviors and the dishonest feedback behaviors from malicious peers, and the issue of inactive recommendations to others. However, these issues cannot be effectively addressed by the existing solutions. Thus, an incentive compatible reputation management model for P2P networks, named ICRM, is proposed to solve them. In ICRM, the metric of time zone is used to describe the time property of the transaction experience and the recommendation. Three other metrics such as the direct trust value, the recommendation trust value and the recommendation credibility, based on the metric of time zone are applied to express accurately the final trust level of a peer. Furthermore, the participating level is introduced as the metric to identify a peer’s activeness degree. Theoretical analysis and simulation experiments demonstrate that, ICRM can effectively suppress the malicious behaviors such as providing unreliable services, or giving dishonest feedbacks to others in the P2P networks. What’s more, it also can incent peers to offer recommendations to others more actively.

VectorTrust: trust vector aggregation scheme for trust management in peer-to-peer networks

With emerging Internet-scale open content and resource sharing, social networks, and complex cyber-physical systems, trust issues become prominent. Conventional trust mechanisms are inadequate at addressing trust issues in decentralized open environments. In this paper, we propose a trust vector based trust management scheme called VectorTrust for aggregation of distributed trust scores. Leveraging a Bellman---Ford based algorithm for fast and lightweight trust score aggregation, VectorTrust features localized and distributed concurrent communication. Built on a trust overlay network in a peer-to-peer network, a VectorTrust-enabled system is decentralized by nature and does not rely on any centralized server or centralized trust aggregation. We design, implement, and analyze trust rating, trust aggregation, and trust management strategies. To evaluate the performance, we design and implement a VectorTrust simulator (VTSim) in an unstructured P2P network. The analysis and simulation results demonstrate the efficiency, accuracy, scalability, and robustness of VectorTrust scheme. On average, VectorTrust converges faster and involves less complexity than most existing trust schemes. VectorTrust remains robust and tolerant to malicious peers and malicious behaviors. With dynamic growth of P2P network scales and topology complexities, VectorTrust scales well with reasonable overheads (about O(lg?N) communication overheads) and fast convergence speed (about O(log? D N) iterations).

A Fuzzy Reputation Management System with Punishment Mechanism for P2P Network

One way to minimize threats in P2P system is to exploit reputation to help evaluate the trustworthiness and predict the future behaviors of peers. In this paper, a set of parameters are identified for describing the trust of a peer. And a trust model based on fuzzy techniques is presented. The trust model uses Amplifier of Malicious Behavior (AMB) to intensify malicious behaviors and helps peers identify the sly malicious peers. The recommendation credibility is evaluated by Recommendation Weight (RW) and Recommendation Accuracy (RA). In simulation, this trust model has been shown to improve the efficiency of P2P system by significantly decreasing the number of inauthentic files on the network.