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Machine Learning Empowered Trust Evaluation Method for IoT Devices
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DOI：10.1109/ACCESS.2021.3076118.png)
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摘要
En 中文
With the rapid development of the Internet of Things (IoT), malicious or affected IoT devices have imposed enormous threats on the IoT environment. To address this issue, trust has been introduced as an important security tool for discovering or identifying abnormal devices in IoT networks. However, evaluating trust for IoT devices is challenging because trust is a degree of belief with regard to various types of trust properties and is difficult to measure. Thus, a machine learning empowered trust evaluation method is proposed in this paper. With this method, the trust properties of network QoS (Quality of Service) are aggregated with a deep learning algorithm to build a behavioral model for a given IoT device, and the time-dependent features of network behaviors are fully considered. Trust is also quantified as continuous numerical values by calculating the similarity between real network behaviors and network behaviors predicted by this behavioral model. Trust values can indicate the trust status of a device and are used for decision making. Finally, the proposed method is verified with experiments, and its effectiveness is described.
Keyword:
Measurement
Internet of Things
Quality of service
Security
Computational modeling
Trust management
Wireless sensor networks
Internet of Things
trust evaluation
network behaviors
machine learning-based method
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3.6
论文数:
9.8W
被引数:
29.4W
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