Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior

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Authors

BANGUI Hind CIOROAICA Emilia GE Mouzhi BÜHNOVÁ Barbora

Year of publication 2023
Type Article in Proceedings
Conference The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23)
MU Faculty or unit

Faculty of Informatics

Citation
Web https://doi.org/10.1145/3555776.3577694
Doi http://dx.doi.org/10.1145/3555776.3577694
Keywords Internet of Behavior;Trust Management;Deep Learning;Autonomous Systems
Description Internet of Behavior (IoB) has emerged as a new research paradigm within the context of digital ecosystems, with the support for understanding and positively influencing human behavior by merging behavioral sciences with information technology, and fostering mutual trust building between humans and technology. For example, when automated systems identify improper human driving behavior, IoB can support integrated behavioral adaptation to avoid driving risks that could lead to hazardous situations. In this paper, we propose an ecosystem-level self-adaptation mechanism that aims to provide runtime evidence for trust building in interaction among IoB elements. Our approach employs an indirect trust management scheme based on deep learning, which has the ability to mimic human behaviour and trust building patterns. In order to validate the model, we consider Pay-How-You-Drive vehicle insurance as a showcase of a IoB application aiming to advance the adaptation of business incentives based on improving driver behavior profiling. The experimental results show that the proposed model can identify different driving states with high accuracy, to support the IoB applications.
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