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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

8

Strana od-do

874-881

Název nakladatele

ACM

Místo vydání

Neuveden

Místo konání akce

Neuveden

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001124308100124