首页|Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

扫码查看
原文链接
  • NSTL
  • Elsevier
In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time-space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users' trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.

Data fusionTrust evaluationTransfer learningDeep reinforcement learningPrivacy preservationInternet of Things

Lin, Hui、Garg, Sahil、Hu, Jia、Wang, Xiaoding、Piran, Md Jalil、Hossain, M. Shamim

展开 >

Fujian Normal Univ, Coll Comp & Cyber Secur, Fuzhou 350117, Fujian, Peoples R China

Ecole Technol Super ETS, Montreal, PQ, Canada

Univ Exeter, Exeter, Devon, England

Sejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea

King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia

展开 >

2022

10.1016/j.inffus.2021.09.001

Information Fusion

Information Fusion

EISCI
ISSN：1566-2535
年,卷(期)：2022.78
  • 3
  • 26