TrueTrust: a feedback-based trust management model without filtering feedbacks in P2P networks

Meng, Xiaohong; Zhang, Ge

doi:10.1007/s12083-019-00742-2

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…where RT t i,j represents the recommended trust value of subjective node i to objective node j in time t, while SRT t i,j and URT t i,j , respectively, represent the total number of successful and failed communications of j measured by the recommended trust measure in time t [18][19][20]. Synthesizing all the malicious attacks that may exist in wireless sensor networks mentioned above, simulate the network environment with great security threats.…”

Section: Recommendation Trust Assessmentmentioning

confidence: 99%

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

Yuan

2022

Wireless Communications and Mobile Computing

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In order to enhance the ability of wireless sensor networks to resist various security threats and reduce the limitations caused by the characteristics of wireless sensor networks and sensor nodes, this paper proposes a secure routing protocol for wireless sensor networks based on trust management. Combined with the relevant parameters of wireless sensor network, the simulation experiment is carried out with MATLAB. Aiming at the trust management part of the wireless sensor network security protocol proposed in this paper, the malicious attack environment such as sensor node attributes is simulated to verify the resistance of this model to relevant malicious attacks. For the trust management-based wireless sensor network security routing protocol proposed in this paper, the model included in the protocol is compared to the existing security routing model, combining the characteristics of average simulation transmission, network life, and average routing update time. Experiments show that the model has better routing performance and has improved by an average of about 20%. We offer a new solution to solve the problem of wireless routing security.

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Section: Recommendation Trust Assessmentmentioning

confidence: 99%

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

Yuan

2022

Wireless Communications and Mobile Computing

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“…Misbehaving nodes are punished based on the distance between misbehaving and requesting nodes. TrueTrust [21] calculates the service responder's credibility as a function of all collected feedback on a service responder without using any feedback filtering mechanisms. However, the main factor in this calculation is the service requester's feedback on a service responder, which reflects the concept of retributive justice.…”

Section: Related Workmentioning

confidence: 99%

AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks

Al-Otaiby

Alhindi

Kurdi

2022

Sensors

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In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

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“…Trade grade Price range Description g 1 (7,10] Special trade (high cost) g 2 (4,7] General trade (normal cost) g 3 [1,4] Simple trade (low cost)…”

Section: Ta B L E 3 Trade Grades Price Ranges and Descriptionsmentioning

confidence: 99%

“…In addition, for data updating, we establish the data update scale function Φ( g i ) for unifying the scale of data updating. This function is expressed in (7).…”

Section: Data Updatementioning

confidence: 99%

“…In this method, the recommendation trust is obtained by using a multilink feedback credibility fusion algorithm, aggregating direct interaction, evaluating similarity and conducting a trust chain transfer calculation. Meng et al 7 proposed a feedback‐based trust management model without filtering feedback in a P2P network, which calculates credibility based on all feedback from a node without using feedback filtering technology. Alhussain et al 8 proposed an enhanced EigenTrust algorithm for peer‐to‐peer network reputation management, which tracks and identifies malicious nodes by analyzing the chain that stores the reputation information and by trading evaluation information of each node.…”

Section: Related Workmentioning

confidence: 99%

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A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

Guo

Yang

Luan

et al. 2021

Concurrency and Computation

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Aiming at overcoming problems such as malicious entities and trust crises in cloud environments, a trust management model based on mutual trust and a rewardwith-punishment mechanism is proposed in this paper. First, according to reputation values of entities and trust relationships among entities, we calculate the comprehensive trust values of a service request user to several candidate service providers and probe these providers with high comprehensive trust values. Second, we select a trade provider according to mutual trust. Finally, after the trade, we update the reputation values and other data of the request user, trade provider, and recommenders based on the final trade result to reward or punish them to various degrees, thus ultimately reducing malicious or dishonest behavior. The experimental results show that our model can effectively identify malicious entities to increase the trade success rate. K E Y W O R D Scloud environment, mutual trust, reputation, reward-with-punishment INTRODUCTIONCloud computing is a new network application model that was proposed by Google in 2006. Cloud computing was developed based on a series of traditional technologies, such as grid computing, distributed systems, peer to peer (P2P), and virtualization. It is a new information technology service resource. It distributes computing tasks over a resource pool that is composed of many computers, thereby enabling various application systems to obtain computing power, storage space, and information services as needed and providing dynamically scalable and cheap computing services on demand through the network. 1,2 Cloud computing can be divided into the following three categories according to the service model: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). 3 However, the widespread application of cloud computing technology also faces many security threats, such as privacy theft, resource misappropriation, virus attacks, and user malicious behavior attacks. The cloud environment is open, and users can add, delete, modify, and check data through the network. If users who passed the identity authentication behave maliciously, a major challenge will be posed to cloud computing security. Therefore, each user's behavior must be evaluated to identify the malicious users.In addition, there is a crisis of trust between cloud service providers and users: Users have doubts regarding whether cloud service providers can provide qualified cloud services in trade (in this paper, when a user buys and uses a provider's service, we say there is a εtradeε between the user and the provider, and the provider is called the trade provider.), while providers also have doubts about users. Therefore, to ensure the smooth operation of trades, the trust relationship between users and providers must be evaluated. Although researchers have proposed various trust models, most models consider only the attitude of user in the trade and ignore that of provider, or do not consider the impact of trade prices ...

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TrueTrust: a feedback-based trust management model without filtering feedbacks in P2P networks

Cited by 14 publications

References 19 publications

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

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