Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

Sahraoui Dhelim, Nyothiri Aung, Huansheng Ning, Luke Chen, Abderrahmane Lakas

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
43 Downloads (Pure)

Abstract

A trust management system (TMS) is an integral component of any Internet of Things (IoT) network. A reliable TMS must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviors of network entities and subsequently judge the reliability of different entities in the IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this article, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Internet of Things
Volume10
Issue number1
DOIs
Publication statusPublished (in print/issue) - 1 Jan 2023

Bibliographical note

Funding Information:
This work was supported in part by the Insight Centre for Data Analytics funded by Science Foundation Ireland under Grant 12/RC/2289 P2; in part by the CONSUS Project funded by the SFI Strategic Partnerships Programme under Grant 16/SPP/3296 and is co-funded by Origin Enterprises Plc; and in part by the National Natural Science Foundation of China under Grant 61872038.

Publisher Copyright:
IEEE

Funding Information:
This work was supported in part by the Insight Centre for Data Analytics funded by Science Foundation Ireland under Grant 12/RC/2289 P2; in part by the CONSUS Project funded by the SFI Strategic Partnerships Programme under Grant 16/SPP/3296 and is co-funded by Origin Enterprises Plc; and in part by the National Natural Science Foundation of China under Grant 61872038.

Publisher Copyright:
© 2014 IEEE.

Keywords

  • IoT
  • trust management
  • network embedding
  • bad-mouthing
  • self-promoting
  • device trust
  • Performance evaluation
  • Smart cities
  • Computational modeling
  • Internet of Things
  • Data collection
  • Reliability
  • Trust management
  • Internet of Things (IoT)
  • Bad-mouthing

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