Abstract
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.
Highlights
As an emerging type of network, the Internet of Things (IoT) has been under development for years. ‘‘Things’’ such as sensors, monitors, and mobile devices are connected via various network technologies
We calculated the mean square error (MSE) and R-squared (R2) values of the models with different data scales to evaluate the performance of regression using (11) and (12): 1n MSE =
4) EXPERIMENT RESULTS AND COMPARISON ANALYSIS: ADVANCED PERFORMANCE COMPARISON AMONG long short-term memory (LSTM)-BASED MODELS Because the accuracy of LSTM-based models is similar, we conducted experiments to compare the performance of LSTM, stacked LSTM, and bidirectional LSTM (Bi-LSTM)
Summary
As an emerging type of network, the Internet of Things (IoT) has been under development for years. ‘‘Things’’ such as sensors, monitors, and mobile devices are connected via various network technologies. Trust mechanisms evaluate the trustworthiness of every entity in a network environment, and the trust level or quantified numerical trust value can be used for data fusion, decision making, and service management [2], [3]. With the development of the notion of edge computing, edge devices can comprehensively and generically collect a network’s QoS information of IoT devices, which is a useful trust property for trust evaluation. Because the proposed approach focusses on evaluating the trustworthiness of IoT devices based on network QoS properties with a numerical trust value, the contributions of this paper include the following: VOLUME 9, 2021. Network behaviors are generic and easy-to-obtain trust properties; in this paper, trust metrics based on comprehensive network behaviors are adopted in trust evaluation.
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