A Hybrid Trust Management Framework for Wireless Sensor and Actuator Networks in Cyber-Physical Systems

Li, Ruidong; Li, Jie; Asaeda, Hitoshi

doi:10.1587/transinf.2013thp0010

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…A clustering analysis of this network of authors shows one big cluster around the US and China, smaller peripheral clusters of European countries collaborating with south-east Asia, and several countries that have not collaborated internationally, and appear as isolated islets in a network representation. Notable examples of these lack of collaboration include Japan with four CRF papers [15], [16], [17] and [18].…”

Section: Descriptive Analysis Of Search Resultsmentioning

confidence: 99%

A systematic review of cyber-resilience assessment frameworks

Estay

Sahay

Barfod

et al. 2020

Computers & Security

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Section: Descriptive Analysis Of Search Resultsmentioning

confidence: 99%

A systematic review of cyber-resilience assessment frameworks

Estay

Sahay

Barfod

et al. 2020

Computers & Security

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“…Finally, the author introduced a time sliding window and a delay function to confirm that the newest data communication has a greater weight. Another good example of a hybrid trust management model (HTMM) is presented in [53] where the authors elaborated on the main challenges with already established trust management frameworks (TMF). The analysis identified two novel attacks i.e., a misbehavior attack and a location-dependent attack against the existing trust establishment technique.…”

Section: Decision Models (Dm)mentioning

confidence: 99%

IoT trust and reputation: a survey and taxonomy

et al. 2023

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IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices’ features, such as device’s role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.

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“…Both academic and industrial fields pay much attention to this problem. To satisfy this requirement, recommender systems have emerged as an effective mechanism to provide suitable recommendation for the costumers about what kinds of the items or persons that they may be potentially interested [7]. At present, there are many popular recommender systems such as the items recommendation in Amazon, the music recommendation in Last.fm, the movies recommendation in Netflix and the Although recommender systems have been widely studied and used both in academic and industrial fields, some important problems still exist.…”

Section: Introductionmentioning

confidence: 99%

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Wan

Xia

Kong

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users.

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A Hybrid Trust Management Framework for Wireless Sensor and Actuator Networks in Cyber-Physical Systems

Cited by 6 publications

References 25 publications

A systematic review of cyber-resilience assessment frameworks

A systematic review of cyber-resilience assessment frameworks

IoT trust and reputation: a survey and taxonomy

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

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