Reputation-based Trust Model Research Articles

A reputation-based trust evaluation model in group decision-making framework

In group decision-making (GDM) problems, experts need to communicate and adjust their opinions in order to achieve consensus on the final decision-making output. Since experts may have conflicting opinions, trust can be critical and an important reference to use in the decision-making process when some experts are required to modify opinions. Recently, decision-making models based on trust and reputation have been investigated intensively. However, these research works usually rely on the constructed social trust network and honesty and fairness of the trust ratings from experts are taken for granted. The objective of this study is to develop a reputation-based trust model for GDM framework to obtain the trust relationship among experts from their direct interaction and word of mouth. First, the paper defines a trust credibility measure to filter out malicious experts before trust assessment, and designs direct trust feedback based on the interaction quality. Then, based on this direct trust feedback, the global reputation model is proposed according to the synthetical performance of received and provided trust feedback, which encourages long-term good behaviour and guarantees trustworthy communications and interactions. The reputation-based trust and direct trust feedback together build trust relationship among experts. Finally, a simulation experimental analysis of the proposed trust and reputation models is carried out to verify their effectiveness in trust and reputation establishment among the experts, even under the presence of malicious ones.

Cutting-Edge Assets for Trust in 5G and Beyond: Requirements, State of the Art, Trends, and Challenges

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, with reputation-based trust models being one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Then, it analyzes pivotal 5G assets on which trust can enhance their performance. In addition, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area.

DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer. Moreover, this network is threatened by attacks like vampire attack where the network is loaded by fake traffic. Here, Dual Encoding Recurrent Neural network (DERNNet) is proposed for classifying the vampire nodes s node in the network. Moreover, the Grey Wolf Optimization (GWO) algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points; thereby maximizing battery/lifetime of the network nodes. The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing (KIDSASR), Risk-aware Reputation-based Trust (RaRTrust) model and Activation Function-based Trusted Neighbor Selection (AF-TNS) in terms of various parameters. These existing methods may lead to wastage of energy due to vampire attack, which further reduce the lifetime and increase average energy consumed in the network. Hence, the proposed DERNNet method achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 92.4% of packet delivery ratio, 85.2% of network lifetime, and 94.3% of classification accuracy.

Toward pre-standardization of reputation-based trust models beyond 5G

In the last years, the number of connections in mobile telecommunication networks has increased rampantly, and in consequence, the number and type of relationships among entities. Should such interactions are to be profitable, entities will need to rely on each other. Hence, mobile telecommunication networks demand trust and reputation models that allow developing feasible communications in 5G and beyond networks, through which a group of entities can establish chains of services between cross-operators/domains, with security and trustworthiness. One of the key obstacles to achieving generalized connectivity beyond 5G networks is the lack of automatized, efficient, and scalable models for establishing security and trust. In this vein, this article proposes a pre-standardization approach for reputation-based trust models beyond 5G. To this end, we have realized a thorough review of the literature to match trust standardization approaches. An abstract set of requirements and key performance indicators has been extracted, and some pre-standardization recommendations proposed to fulfill essential conditions of future networks and to cover the lack of common trust and reputation models beyond 5G.

Effect of Latency for a Reputation Based Trust Model of Peer-to-Peer Content Distribution Networks

Effect of Latency for a Reputation Based Trust Model of Peer-to-Peer Content Distribution Networks

GroupTrust: Dependable Trust Management

As advanced computing and communication technologies penetrate every aspect of our life, we have witnessed the persistent growth of open systems where entities interact with one another without prior knowledge or experiences. Trust becomes an important metric in such open systems. This paper presents a dependable trust management scheme-GroupTrust, and a working system to support GroupTrust. It makes three original contributions. First, we identify a set of vulnerabilities that are common in existing reputation based trust models. We show that reputation trust built solely on direct experiences or by combining direct experiences with uniform trust propagation can be vulnerable. Second, we develop GroupTrust, a dependable trust management scheme to provide reliable trust management in the presence of dishonest ratings, malicious camouflage, and malicious collusive behaviors. The GroupTrust scheme is novel in two aspects: (i) we develop a pairwise similarity based feedback credibility to enhance the resilience of trust computation in the presence of dishonest ratings; (ii) we propose to propagate trust based on a Susceptible-Infected-Recovered (SIR) model, which defines trust propagation threshold to control how trust should be propagated. Finally, we evaluate the effectiveness of GroupTrust against fourthreat models using both simulated and real world datasets. Our experimental results show that feedback credibility based local trust computation can effectively constrain strategically malicious participants from taking advantages of their dishonest ratings. SIR-based trust propagation control enables safe trust propagation and blocks irrational trust propagation. We show that GroupTrust scheme significantly outperforms other trust models in terms of both performance and attack resilience in the presence of dishonest feedbacks, sparse feedbacks, and strategically malicious participants against four representative threat models.

TrustCall: A Trust Computation Model for Web Conversational Services

Web conversational services are exposed to several threats in which the social context between communicating participants is manipulated. Cybercrimes based on identity misrepresentation to obtain sensitive information are on the rise. Various scams and frauds are conducted by distributing malicious content, viruses, and spam over established communication sessions. In order to maintain overall security and enhance privacy, methods of estimating trustworthiness and reputation should be built into Web calling services. In this paper, we propose “TrustCall” a reputation-based trust model for real-time Web conversational services. In our approach, the reputation of a caller is evaluated using Authenticity Trust and Behavioral Trust . Authenticity Trust describes the legitimacy of a caller by collecting recommendations from other members of the network, whereas Behavioral Trust determines a caller’s popularity based on its communication behavior. Other contributions include a threat taxonomy for Web calling services, including social threats, that directly target users. A set of experiments are conducted in order to prove the feasibility and effectiveness of our model.

Trust aware cooperative optimised link state routing protocol

Optimised link state routing OLSR protocol is a popular proactive routing protocol for wireless ad-hoc networks WANETs. The functionality of OLSR protocol such as selection of multi-point relays, controlling the flooding of link state information and preparation of the routing table is depending upon the cooperation among the nodes in the network. However, open and decentralised network operations of WANETs introduce various security vulnerabilities to disrupt the routing process. To this end, this paper proposes a reputation-based trust model to enhance the cooperation among nodes in the routing process. The proposed trust model calculates the consolidate trust value CTV using direct and indirect observations. This CTV is used in the OLSR process to identify and isolate the misbehaving nodes from the routing path without having additional overhead. The simulation results show the effectiveness of the proposed method.

Survey on CommTrust: Multi-DimensionalTrust Using Mining E-Commerce FeedbackComments

The Reputation based trust models are very admirable in ecommerce applications .Feedback ratings are gathered together for computing sellers’ reputation trust scores. A CommTrust system is proposed where the observation made by buyers are mostly used to express opinions about the product in free text feedback review. These feedback review comments are mined. In CommTrust 1) a multidimensional trust model is proposed for computing reputation scores from user feedback comments, 2) for mining feedback comments which are used for weights and ratings of dimension an algorithm is proposed; natural language processing’s combining techniques, opinion mining, and topic modeling. The CommTrust proved to be very effective after testing it on various websites like Amazon and eBay.

Self-nominating trust model based on hierarchical fuzzy systems for peer-to-peer networks

Security is a critical constraint for the expansion of Peer-to-Peer (P2P) networks. The autonomy, dynamic and distribution natures benefit both valid and malicious users and also lead that P2P networks are extremely susceptible to malicious users. Exploiting a reputation-based trust model is a feasible solution in such an open environment to build trust relationship among peers. While most of existing trust models focus on restraining the abuse and malicious attacks, intentions and sharing capabilities of peers are mostly ignored. In this paper, we present a self-nominating trust model based on hierarchical fuzzy systems to quantify the behaviors of peers. The reputation is defined based on eight factors, where three promising factors are provided by resource holders to demonstrate their desires, and four capability factors are recorded by requesters to identify the provider’s service capability. The approach degree based updating recommendation is deployed to aggregate the global trust metrics. Experimental results illustrate that our trust model effectively improves the efficiency and security of P2P systems.

Enhanced Detection System for Trust Aware P2P Communication Networks

Botnet is a number of computers that have been set up to forward transmissions to other computers unknowingly to the user of the system and it is most significant to detect the botnets. However, peer-to-peer (P2P) structured botnets are very difficult to detect because, it doesn’t have any centralized server. In this paper, we deliver an infrastructure of P2P that will improve the trust of the peers and its data. In order to identify the botnets we provide a technique called data provenance integrity. It will ensure the correct origin or source of information and prevents opponents from using host resources. A reputation based trust model is used for selecting the trusted peer. In this model, each peer has a reputation value which is calculated based on its past activity. Here a hash table is used for efficient file searching and data stored in it is based on the reputation value.

CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The “all good reputation” problem, however, is prevalent in current reputation systems-reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.

A Fuzzy Reputation-based Trust Management Scheme for Cloud Computing

Cloud computing requires that customers trust that a service provider’s platforms are secured and provide a sufficient level of integrity for the client’s data. However, a lack of trust between cloud customers and providers has hindered the universal acceptance of clouds as an increasingly popular approach for the processing of large data sets and computationally expensive programs. Existing techniques do not satisfactorily solve the problem, a good solution is to leverage trust management technology to build trust into utility cloud computing. Recently, trust modeling is paid more and more attention in cloud computing. Therefore, in the paper we propose a fuzzy reputation-based trust model (FRTM) based on fuzzy logic inferences. The main idea of our trust model is to use fuzzy logic inferences to handle uncertainty, fuzziness, and incomplete information in cloud trust reports. The proposed trust model will help cloud customers determine the level of trust that is to be placed on any particular cloud service provider. Experiments and results demonstrate the accuracy of the proposed trust model. Through analysis and experiments, we believe our approach is useful for cloud customers to make the decision regarding who to interact with.

The Chain Effect for the Reputation-Based Trust Model in Peer-to-Peer Computing

This letter analyzes a resource chain trust model for P2P reputation-based systems. Many researchers have given a lot of efforts to reputation-based system area and some of them have made good theoretical models. Problems are to spread malicious contents whereas the remark that such models only concentrate on the relationship between the node and its direct neighbors is still controversial. To solve the problems, we introduced the RCM (Resource Chain Model) and the Enhanced RCM. In this letter, we analyze the models and then show usage of our models can help us to find the best and safest location efficiently and decrease the number of malicious transaction.

Effective Usage of Computational Trust Models in Rational Environments

Computational reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. In this article, we study the relation between their accuracy measures and their ability to enforce cooperation among participants and discourage selfish behaviors. We provide theoretical results that show the conditions under which cooperation emerges when using computational trust models with a given accuracy, and how cooperation can still be sustained while reducing the cost and accuracy of those models. Specifically, we propose a peer selection protocol that uses a computational trust model as a dishonesty detector to filter out unfair ratings. We prove that such a model with reasonable misclassification error bound in identifying malicious ratings can effectively build trust and cooperation in the system, considering rationality of participants. These results reveal two interesting observations. First, the key to the success of a reputation system in a rational environment is not a sophisticated trust-learning mechanism, but an effective identity-management scheme to prevent whitewashing behaviors. Second, given an appropriate identity-management mechanism, a reputation-based trust model with a moderate accuracy bound can be used to effectively enforce cooperation in systems with both rational and malicious participants. As a result, in heterogeneous environments where peers use different algorithms to detect misbehavior of potential partners, cooperation may still emerge. We verify and extend these theoretical results to a variety of settings involving honest, malicious, and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer, electronic commerce, or community systems.

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.

A Comprehensive Reputation Based Trust Model in C2C Electronic Commerce

The mistrust between buyers and sellers in C2C electronic commerce has been affecting its development. The accuracy and the reliability of the reputation system are still not high because of the existence of vicious users. This paper builds a comprehensive reputation based trust model by choosing the suitable influential factors in trust and the simulation results prove that this model has a higher accuracy and reliability.

A formal context specific trust model (FTM) for multimedia and ubiquitous computing environment

In order to ensure secure sharing of resources in an ad-hoc network of handheld devices in a multimedia and ubiquitous computing environment, mutual collaboration is essential. However, the limitations, such as poor storage and computational capability of these multimedia and ubiquitous devices stand as the bottleneck for effective sharing of resources. As a result of this drawback, the adversaries are obtaining access to the new doors for security breaches. Mutual Trust is the weapon used to combat security violations by restricting malicious devices from participating in any interaction in such an open and dynamic environment. In this paper, we present a context-specific and reputation-based trust model along with a brief survey and a comparative study of the existing trust models found in the literature. To the best of our knowledge, our model is the first formal trust model for multimedia and ubiquitous computing, which incorporates multi-hop recommendation capability and flexible behavioral model to handle interactions among devices. The other major contribution of this universal trust model is a simple protocol for circumventing malicious recommendations and handling them for achieving accuracy. This paper also illustrates the implementation and evaluation detailed of our proposed omnipresent formal trust model.

Fuzzy reputation-based trust model

We present PATROL-F (comPrehensive reputAtion-based TRust mOdeL with Fuzzy subsystems) as a comprehensive model for reputation-based trust incorporating fuzzy subsystems to protect interacting hosts in distributed systems. PATROL-F is the fuzzy version of our previous model PATROL, and aims at achieving a truly unique model incorporating various concepts that are important for the calculation of reputation values and the corresponding decisions of whether or not to trust. Among the incorporated concepts are direct experiences and reputation values, the credibility of a host to give recommendations, the decay of information with time based on a dynamic decay factor, first impressions, and a hierarchy of host systems. The model also implements the concepts of similarity, popularity, activity, and cooperation among hosts. In addition, PATROL-F's fuzzy subsystems account for humanistic and subjective concepts such as the importance of a transaction, the decision in the uncertainty region, and setting the result of interaction. We present simulations of PATROL-F and show its correctness and reliability.

Design, analysis, and deployment of omnipresent Formal Trust Model (FTM) with trust bootstrapping for pervasive environments

The rapid decrease in the size of mobile devices, coupled with an increase in capability, has enabled a swift proliferation of small and very capable devices into our daily lives. With such a prevalence of pervasive computing, the interaction among portable devices needs to be continuous and invisible to device users. As these devices become better connected, collaboration among them will play a vital role in sharing resources in an ad-hoc manner. The sharing of resources works as a facilitator for pervasive devices. However, this ad hoc interaction among devices provides the potential for security breaches. Trust can fight against such security violations by restricting malicious nodes from participating in interactions. Therefore, we need a unified trust relationship model between entities, which captures both the needs of the traditional computing world and the world of pervasive computing where the continuum of trust is based on identity, physical context or a combination of both. Here, we present a context specific and reputation-based trust model along with a brief survey of trust models suitable for peer-to-peer and ad-hoc environments. This paper presents a multi-hop recommendation protocol and a flexible behavioral model to handle interactions. One other contribution of this paper is the integration of an initial trust model; this model categorizes services or contexts in different security levels based on their security needs, and these security needs are considered in trust bootstrapping. The other major contribution of this paper is a simple method of handling malicious recommendations. This paper also illustrates the implementation and evaluation of our proposed formal trust model.