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

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.

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

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: 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
• Tags: firank_A
• Tags: International impact, Reviewed

Abstract

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