Representation of Trust and Reputation in Self-Managed Computing Systems

Kantert, Jan; Edenhofer, Sarah; Tomforde, Sven; Müller-Schloer, Christian

doi:10.1109/cit/iucc/dasc/picom.2015.273

Cited by 17 publications

(17 citation statements)

References 19 publications

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“…the WU is calculated correctly and returned in time). For more details see [39]. During this self-organising process, uncertainty is reduced and trust-relationships are established among the agents.…”

Section: Life-cycle Of An Etcmentioning

confidence: 99%

Trust Communities: An Open, Self-Organised Social Infrastructure of Autonomous Agents

Edenhofer

Tomforde

Kantert

et al. 2016

Trustworthy Open Self-Organising Systems

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Future technical systems will be increasingly characterised by openness and heterogeneity of participating elements. Based on exemplary application scenarios such as Desktop Computing Grids, Smart Power Grids, and Networked Camera Systems, this chapter develops a solution perspective to handle anomalies, disturbances, and malicious behaviour by making use of trust and reliability measures in self-organised systems. The overall goal is to increase the robustness of open distributed systems with low overhead. Therefore, a novel self-organised multi-agent organisation-the Trust Community-is introduced in two variants: as implicit and as explicit self-structuring society of autonomous agents. For the explicit variant, a life-cycle and management routines are described. For evaluation purposes, we simulate a Trusted Desktop Computing Grid and introduce different types of stereo-type agent behaviour, ranging from altruistic to egoistic and to cunning behaviour. In order to support efficiency and stabilise the process, we show the benefits of explicit Trust Communities, which results in significantly lower overhead and more reliable relations among agents compared to other forms of agent societies.

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Section: Life-cycle Of An Etcmentioning

confidence: 99%

Trust Communities: An Open, Self-Organised Social Infrastructure of Autonomous Agents

Edenhofer

Tomforde

Kantert

et al. 2016

Trustworthy Open Self-Organising Systems

Self Cite

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“…Kantert et al [24] proposed a self-maintenance trust management system, which enables autonomous servers from different administrative domains to share their resources in grid-like situations. Contrary to other grid deployments, it assumes that there are selfish individuals or malicious service providers.…”

Section: Relevant Workmentioning

confidence: 99%

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

Kong

Hou

et al. 2020

Electronics

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With the development of 5G, user terminal computing moves up and cloud computing sinks, thus forming a computing fusion at the edge. Edge computing with high-efficiency, real-time, and fast features will become part of 5G construction. Utilizing distributed computing and storage resources at the edge of the network to perform distributed data processing tasks can alleviate the load on the cloud computing center, which is also the development trend of edge computing. When a malicious node exists, the error information feedback by the node will affect the result of local perception decision. To solve the problem of malicious behavior of the node, a node trust evaluation mechanism of interactive behavior is introduced. The trust mechanism for edge computing network environments is introduced as a novel security solution. First, the key thought of the trust mechanism proposed in this paper is to establish a trust relationship between edge nodes in open edge computing environment. Then, a multi-source trust fusion algorithm based on time decay aggregates direct interaction trust and different third-party recommendation trust to calculate the global trust of the evaluated nodes. Finally, simulation experiments show that the algorithm has a certain degree of improvement in computational efficiency and interaction success rate over other existing models, which reduces the situation of malicious node deception.

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“…Trust management has been studied in other fields (Shang-Fu and Jian-Lei, 2012;Yan et al, 2014;Corradini et al, 2015). For instance, Kantert et al (2015) proposed a self-managed trust management system, which uses trust metrics calculated in a distributed manner, while respecting autonomy. Other quantitative trust management components have been developed and integrated with existing platforms such as the safety on untrusted network devices.…”

Section: Ics 285mentioning

confidence: 99%

A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

Zineddine

2020

ICS

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Purpose Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network. Design/methodology/approach The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm. Findings The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network. Research limitations/implications Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available. Originality/value The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

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Representation of Trust and Reputation in Self-Managed Computing Systems

Cited by 17 publications

References 19 publications

Trust Communities: An Open, Self-Organised Social Infrastructure of Autonomous Agents

Trust Communities: An Open, Self-Organised Social Infrastructure of Autonomous Agents

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

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