A two-way trust management system for fog computing

Alemneh, Esubalew; Senouci, Sidi‐Mohammed; Brunet, Philippe; Tegegne, Tesfa

doi:10.1016/j.future.2019.12.045

Cited by 73 publications

(29 citation statements)

References 60 publications

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“…Overall indirect trust can be obtained by applying discounting and consensus operators together on the received recommendations as subjective trust [65]. Suppose a Fog node has a list of recommenders and has trust values about them at time t as T t i,r1 , T t i,r2 , .…”

Section: Evolving Trust Using Subjective Logicmentioning

confidence: 99%

“…It is not feasible for an evaluator node to identify all related trust metrics to assess another node’s trust value, and this will lead to the fact that the trust is calculated with insufficient evidence [ 62 , 63 ]. The subjective logic is suggested to be the most appropriate for modeling trust in the fog computing environment [ 64 , 65 ] as each node computes another node’s trust value it encounters subjectively. Subjective logic utilizes opinions as input and output variables.…”

Section: Trust Model Mappingmentioning

confidence: 99%

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Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Ghaleb

Azzedin

2021

Sensors

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The Internet of Services (IoS) is gaining ground where cloud environments are utilized to create, subscribe, publish, and share services. The fast and significant evolution of IoS is affecting various aspects in people’s life and is enabling a wide spectrum of services and applications ranging from smart e-health, smart homes, to smart surveillance. Building trusted IoT environments is of great importance to achieve the full benefits of IoS. In addition, building trusted IoT environments mitigates unrecoverable and unexpected damages in order to create reliable, efficient, stable, and flexible smart IoS-driven systems. Therefore, ensuring trust will provide the confidence and belief that IoT devices and consequently IoS behave as expected. Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. In this article, we propose scalable and efficient Chord-based horizontal architecture. We also show how trust modeling can be mapped to our proposed architecture. Extensive performance evaluation experiments have been conducted to evaluate the performance and the feasibility and also to verify the behavior of our proposed architecture.

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Section: Evolving Trust Using Subjective Logicmentioning

confidence: 99%

Section: Trust Model Mappingmentioning

confidence: 99%

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Ghaleb

Azzedin

2021

Sensors

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“…The scheme countermeasures the malicious nodes which aim to degrade the network by spreading false and forged data. Alemneh et al [19] propose a TMS where both service requester and service provider can evaluate each other's reliability prior to any interaction. The trust model is based on subjective logic and mitigates malicious nodes' behaviour to manipulate the trust results.…”

Section: Trust Modelsmentioning

confidence: 99%

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

Junejo

Komninos

McCann

2021

IEEE Internet Things J.

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Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities' performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC's access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework's design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.

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“…Authors have provided some recommendations that can help in addressing, at least partially, the issue of trust in the Fog environment such as using blockchain technology to maintain the log files, which has the records of events and messages exchanging in Fog nodes in a distributed way. 84,[106][107][108][109][110][111] So, if a Fog node misbehaves, other trusted nodes can discover that. In other words, using Blockchain can enable a trustworthy IoT-Fog-Cloud architecture.…”

Section: Trust Managementmentioning

confidence: 99%

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Alzoubi

Osmanaj

Jaradat

et al. 2020

Security and Privacy

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Fog nodes are implemented near to end‐users Internet of Things (IoT) devices, which mitigate the impact of low latency, location awareness, and geographic distribution unsupported features of many IoT applications. Moreover, Fog computing decreases the data offload into the Cloud, which decreases the response time. Despite these benefits, Fog computing faces many challenges in meeting security and privacy requirements. These challenges occur due to the limitations of Fog computing resources. In fact, Fog computing may add new security and privacy issues. Although many papers have discussed the Fog security and privacy issues recently, most of these papers have discussed these issues at a very high level. This paper provides a comprehensive understanding of Fog privacy and security issue. In this survey, we review the literature on Fog computing to draw the state‐of‐the‐art of the security and privacy issues raised by Fog computing. The findings of this survey reveal that studying Fog computing is still in its infant stage. Many questions are yet to be answered to address the privacy and security challenges of Fog computing.

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A two-way trust management system for fog computing

Cited by 73 publications

References 60 publications

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

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