Global Reputation Values Research Articles

Blockchain-Based Information Sharing Security for the Internet of Things

The Blockchain (BCT) is the first decentralized ledger to include a trust mechanism in its design. It establishes a trustworthy framework for distributed commands by using data redundancy at several nodes. Conspicuously, the current study presents a BCT-based lightweight IoT information exchange security architecture for data exchange. The proposed technique uses a dual chain methodology, namely transaction and data BCT working together to provide distributed storage and tamper-proofing of data. Moreover, Transaction BCT is enhanced by a consensus algorithm using a practical Byzantine fault-tolerant (PBFT) mechanism. The proposed algorithm can increase data registering efficiency, transactions, and privacy protection BCT. It is deduced that local dominance can be avoided using the dynamic game strategy of node cooperation. Furthermore, by reporting the node’s global reputation value, the status of the unknown node may be approximated. The high-trust measure is utilized to adjust the weight of the affected node in the combined node-set, leading to the Bayesian equilibrium. The proposed model is validated in several experimental simulations and results are compared with state-of-the-art techniques. Based on the results, enhanced performance is registered for the proposed techniques in terms of temporal delay, statistical efficiency, reliability, and stability.

Supply chain security evaluation model and index system based on a 5G information system.

Currently, the supply chain management model has been widely adopted worldwide. Internationally renowned companies such as IBM and Walmart have made significant achievements in supply chain management and have played a greater role in promoting the growth of their interests. However, while they are enjoying their own interests, they are also facing various risks in the supply chain. From the perspective of systems engineering, this paper uses complex network theory as a research tool to study supply chain networks and systematically study the important issues of supply chain network risks. This paper analyzes the new characteristics of the relationship between enterprises and suppliers in the supply chain and which indicators should be selected as the basis for evaluating suppliers from the perspective of cooperation and development, which more comprehensively reflects the characteristics of the new enterprise–supplier relationship in the supply chain environment. This highlights the evaluation of the supplier’s cooperation ability and development ability. Aiming at the problem of supply chain network system identification and integrated evaluation, a supply chain security evaluation model and index system research framework model are proposed, and the intercompany business risk and supply chain in supply chain network evaluation are introduced. As a model for evaluating transition parameters, the study of supply chain risk propagation introduces the theory of dynamic propagation in complex networks, the law of evolution, and the supplier's dynamic supply chain risk model. The experimental results of this paper show that when the global reputation value is 0.5 and the local reputation value is 0.8 and 0.7, the trust degree changes with the change of the confidence factor; when the value of the confidence factor decreases, the degree of trust converges more and more to the global reputation and vice versa. It can be seen that this model has high feasibility.

A Dynamic Local–Global Trust-aware Recommendation approach

Abstract Trust-aware recommender systems have attracted much attention in recent years because of the popularity of social networks. Some researchers have proposed local trust models to measure trust between two users based on their interactions. However, in most cases, there are few (if any) direct interactions between two users. In such cases, it is useful to associate a user with a global reputation value that reflects the experience of the whole community with that user. In this paper, we propose a Dynamic Local–Global Trust-aware Recommendation (DLGTR) approach, which uses a new hybrid model of local and global trust. In this hybrid model, the relative importance of local trust versus global trust is dynamically determined based on the reliability of information from the two trust models. Moreover, DLGTR uses an incremental method to efficiently update a trust network as new data become available. This allows DLGTR to constantly improve the recommendation quality without loss of efficiency. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art recommendation approaches, especially in terms of prediction accuracy and computational time.

EERP: An enhanced EigenTrust algorithm for reputation management in peer-to-peer networks

Open peer-to-peer (P2P) networks have many applications in large-scale distributed systems, internet computing, file sharing and social networks. Peers in P2P networks share data or computing resources while being strangers to each other. In many cases, peers are anonymous volunteers in a dynamic environment where peers can join and leave the network at any time. The open nature of P2P networks is an ideal environment for malicious peers to spread their inauthentic files. Furthermore, some malicious peers form groups to mislead the rest of the peers by giving malicious friends within their groups faked high trust values to increase their reputations. These challenges increase the need to manage the reputation schemes in P2P networks. This paper proposes the EERP algorithm, which focuses on malicious peers and how they can be identified and isolated from the network. This is done by saving the reputation information of each peer as a chain that contains, in addition to the peer’s global reputation value, the rating given to this particular peer after each transaction they are involved in and the identity of the peer who gave the rating. Analysing these logs of peers’ histories helps to track and identify malicious peers. EERP has the potential to effectively reduce the number of inauthentic files spread in a P2P network as compared with other trust management systems.

SIP-based VoIP anomaly detection engine using DTV and ONR

VoIP has gained more attention in recent years due to its advantage of cheap calls when compared to the existing PSTN network. The callers such as the advertiser, telemarketers, prank callers who make use of this VoIP for generating the anomaly calls and messages are characterised as SPIT. The previous work detects the spam caller after getting the feedback from the callee. The proposed technique detects the anomaly in the call pattern without user involvement, i.e., the pre-acceptance method. This SIP-based approach relies on DTV and ONR of the caller to detect the anomaly calls and block the spammer. The parameter call duration, call count with frequency and the unique partner of the caller are used to compute the direct trust value of VoIP user. The ONR depicts the user behaviour in the digital shopping. The online shopping behaviour of the sender insists on the ONR value. The aggregation algorithm uses the DTV and ONR to measure the global reputation of the caller. This calculated global reputation value detects the anomalies and segregate the non-legitimate user during call setup using the session initiation protocol. The proposed system detects the spammer without analysing the content, without getting feedback from the user and before connecting the call.

P2P trust model for resisting collusion and dynamic behavior of node

To solve the problems of great network costs,the deficiency in dealing with dynamic characteristic and collusion attack in current P2P trust model,a new trust model was proposed.It computed global reputation value through that the direct interaction peers' local evaluation weighted its evaluation credibility to avoid iterative computing process.The standard deviation and concentration ratio of local evaluation was used to identify and control collusion attack.Moreover,according to the node changes,its reputation value and evaluation credibility were updated dynamically.The simulation and experimental results indicate that the new model has advantages in reducing network costs,controlling dynamic characteristic of node and collusion attack over the existing trust models.

A Reputation-Based Multi-Agent Model for Network Resource Selection

Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.