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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130329" target="_blank" >RIV/00216224:14330/23:00130329 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3555776.3577694" target="_blank" >https://doi.org/10.1145/3555776.3577694</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3555776.3577694" target="_blank" >10.1145/3555776.3577694</a>

Result language
angličtina
Original language name
Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior
Original language 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.
Czech name
—
Czech description
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Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23)
ISBN
9781450395175
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
874-881
Publisher name
ACM
Place of publication
Neuveden
Event location
Neuveden
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001124308100124

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