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

D 2023

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

BANGUI, Hind, Emilia CIOROAICA, Mouzhi GE and Barbora BÜHNOVÁ

Basic information

Original name

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

Authors

BANGUI, Hind (504 Morocco, guarantor, belonging to the institution), Emilia CIOROAICA (642 Romania), Mouzhi GE (156 China) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution)

Edition

Neuveden, The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23), p. 874-881, 8 pp. 2023

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/23:00130329

Organization unit

Faculty of Informatics

ISBN

978-1-4503-9517-5

DOI

http://dx.doi.org/10.1145/3555776.3577694

UT WoS

001124308100124

Keywords in English

Internet of Behavior;Trust Management;Deep Learning;Autonomous Systems

Abstract

V originále

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.

Links

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(CEP code: EF16_019/0000822)

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Acronym: C4e)

Investor: Ministry of Education, Youth and Sports of the CR, CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence, Priority axis 1: Strengthening capacities for high-quality research

EF16_019/0000822, research and development project

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

BANGUI, Hind, Emilia CIOROAICA, Mouzhi GE and Barbora BÜHNOVÁ. Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior. Online. In The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23). Neuveden: ACM, 2023, p. 874-881. ISBN 978-1-4503-9517-5. Available from: https://dx.doi.org/10.1145/3555776.3577694.

@inproceedings{2255157,
   author = {Bangui, Hind and Cioroaica, Emilia and Ge, Mouzhi and Bühnová, Barbora},
   address = {Neuveden},
   booktitle = {The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23)},
   doi = {http://dx.doi.org/10.1145/3555776.3577694},
   keywords = {Internet of Behavior;Trust Management;Deep Learning;Autonomous Systems},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-1-4503-9517-5},
   pages = {874-881},
   publisher = {ACM},
   title = {Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior},
   url = {https://doi.org/10.1145/3555776.3577694},
   year = {2023}
}

TY  - JOUR
ID  - 2255157
AU  - Bangui, Hind - Cioroaica, Emilia - Ge, Mouzhi - Bühnová, Barbora
PY  - 2023
TI  - Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior
PB  - ACM
CY  - Neuveden
SN  - 9781450395175
KW  - Internet of Behavior;Trust Management;Deep Learning;Autonomous Systems
UR  - https://doi.org/10.1145/3555776.3577694
N2  - 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.
ER  -

BANGUI, Hind, Emilia CIOROAICA, Mouzhi GE and Barbora BÜHNOVÁ. Deep-Learning Based Trust Management with Self-Adaptation in the Internet of Behavior. Online. In \textit{The 38th ACM/SISAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingGAPP Symposium on Applied Computing (SAC '23)}. Neuveden: ACM, 2023, p.~874-881. ISBN~978-1-4503-9517-5. Available from: https://dx.doi.org/10.1145/3555776.3577694.

Detailed Information on Publication Record