CRM: A New Dynamic Cross-Layer Reputation Computation Model in Wireless Networks

Lin, Hui; Hu, Jia; Ma, Jianfeng; Xu, Li; Yang, Li

doi:10.1093/comjnl/bxu075

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Rivas et al [11] considered all credit evaluation factors obtained from neighboring vehicles, which may lead to inaccurate credit evaluation and even security problems such as collusion attacks. Lin et al [12] put forward the reputation model based on subjective logic, which mainly uses the theory of subjective logic to quantify the reputation information of nodes. The subjective logic defines the representation and calculation methods of credibility quantitatively.…”

Section: Iov Reputationmentioning

confidence: 99%

A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles

Du,

Zhang,

et al. 2023

Applied Sciences

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As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicles and reduce the probability of malicious nodes participating in the consensus, we propose to calculate the reputation of RSU nodes based on multi-weighted subjective logic (CRMWSL) model. Secondly, in order to ensure the efficiency of blockchain data consensus, we improve the consensus protocol of traditional double-layer PBFT, change the election method of the committee and the PBFT consensus process, and improve throughput by reducing the number of consensus nodes. For the committee, we combine the credibility value and hash method to ensure the credibility of nodes, but also to ensure a certain degree of election randomness. For the PBFT consensus process, the regional committee consensus is carried out first, and then the regional master node carries out the global consensus. Through experimental comparison, we show that the C-PBFT significantly reduces consensus time, network overhead, and is scalable for Internet of Vehicles.

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Section: Iov Reputationmentioning

confidence: 99%

A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles

Du,

Zhang,

et al. 2023

Applied Sciences

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“…In the MCC architecture, the application cloud server informs all mobile clients about their assigned data tasks and distributes tasks to mobile clients who meet the requirements of applications. This paper focuses on the internal security threats [25][26][27] that can affect data trustworthiness. The internal threats are launched by an inside attacker who is a legal and certified mobile client.…”

Section: The Adversary Modelmentioning

confidence: 99%

“…In this section, we elaborate on the proposed Data Trustworthiness enhanced Reputation Mechanism (DTRM), which integrates the reputation mechanisms [18,25,27] with the mobile crowd sensing (MCS) [15], metagraph theory [31], data category and user group division technologies [32] to enhance data trustworthiness, defend against the insider threat and enhance the big data veracity in MCC. The DTRM is implemented in mobile sensing devices and cloud service providers to perform bidirectional reputation evaluation.…”

Section: A Data Trustworthiness Enhanced Reputation Mechanism (Dtrm)mentioning

confidence: 99%

DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing

Lin

et al. 2018

Future Generation Computer Systems

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Cloud computing and the mobile Internet have been the two most influential information technology revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large-scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure an important but often overlooked aspect of big data --data veracity. Troublesome internal attacks launched by internal malicious users is one key problem that reduces data veracity and remains difficult to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory based user group division, and reputation transferring methods are integrated into the reputation query and evaluation process. The extensive simulation results based on real datasets show that the DTRM outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.

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“…This paper focuses on the internal security threats [13,14] in MCC that can affect data veracity. The internal threats are launched by an inside attacker who is a legal and certified user.…”

Section: B Adversary Modelmentioning

confidence: 99%

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin

Tian

et al. 2017

Information Sciences

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Abstract:As a promising next-generation computing paradigm, Mobile Cloud Computing (MCC) enables the large-scale collection and big data processing of personal private data. An important but often overlooked V of big data is data veracity, which ensures that the data used are trusted, authentic, accurate and protected from unauthorized access and modification. In order to realize the veracity of data in MCC, specific trust models and approaches must be developed. In this paper, a Category-based Context-aware and Recommendation incentive-based reputation Mechanism (CCRM) is proposed to defend against internal attacks and enhance data veracity in MCC. In the CCRM, innovative methods, including a data category and context sensing technology, a security relevance evaluation model, and a Vickrey-Clark-Groves (VCG)-based recommendation incentive scheme, are integrated into the process of reputation evaluation. Cost analysis indicates that the CCRM has a linear communication and computation complexity. Simulation results demonstrate the superior performance of the CCRM compared to existing reputation mechanisms under internal collusion attacks and bad mouthing attacks.

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CRM: A New Dynamic Cross-Layer Reputation Computation Model in Wireless Networks

Cited by 6 publications

References 28 publications

A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles

A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles

DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

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