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 a Barbora BÜHNOVÁ

Základní údaje

Originální název

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

Autoři

BANGUI, Hind (504 Maroko, garant, domácí); Emilia CIOROAICA (642 Rumunsko); Mouzhi GE (156 Čína) a Barbora BÜHNOVÁ (203 Česká republika, domácí)

Vydání

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

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/23:00130329

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-9517-5

DOI

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

UT WoS

001124308100124

EID Scopus

2-s2.0-85162918126

Klíčová slova anglicky

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

Štítky

firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 7. 4. 2024 22:49, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

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

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Akronym: C4e)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur, PO 1 Posilování kapacit pro kvalitní výzkum

EF16_019/0000822, projekt VaV

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

Citovat

BANGUI, Hind; Emilia CIOROAICA; Mouzhi GE a 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, s. 874-881. ISBN 978-1-4503-9517-5. Dostupné z: 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  - CONF
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 a 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, s.~874-881. ISBN~978-1-4503-9517-5. Dostupné z: https://dx.doi.org/10.1145/3555776.3577694.

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