Edge Intelligence for Real-Time IoT Service Trust Prediction
Author:
Affiliation:
1. School of Computing Technologies, RMIT University, Melbourne, VIC, Australia
2. Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture
Link
http://xplorestaging.ieee.org/ielx7/4629386/10213383/10036140.pdf?arnumber=10036140
Reference37 articles.
1. A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique
2. N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
3. Modelling trust dynamics in the Internet of Things
4. SC-TRUST: A Dynamic Model for Trustworthy Service Composition in the Internet of Things
5. Adaptive Trust: Usage-Based Trust in Crowdsourced IoT Services
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3. A Comprehensive Review for 4-D Trust Management in Distributed IoT;IEEE Internet of Things Journal;2023-12-15
4. Federated Learning-driven Trust Prediction for Mobile Edge Computing-based IoT Systems;2023 IEEE International Conference on Web Services (ICWS);2023-07
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