| Title: |
Adaptive Authentication Factor Selection in the Internet of Things: A Trust-Based Multi-Objective Optimization Approach |
| Authors: |
Saideh, Marc; Jamont, Jean-Paul; Vercouter, Laurent |
| Contributors: |
Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie); Institut National des Sciences Appliquées (INSA)-Normandie Université (NU); Equipe Multi-agent, Interaction, Décision (LITIS - MIND); Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (LITIS); Université Le Havre Normandie (ULH); Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN); Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie); Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH); Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA); Laboratoire de Conception et d'Intégration des Systèmes (LCIS); Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA); Université Grenoble Alpes (UGA); Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS); ANR-21-CE23-0016,MaestrIoT,Processus de décision multi-agent de confiance pour l'Internet des Objets(2021) |
| Source: |
The Seventeenth Workshop on Adaptive and Learning Agents ; https://hal.science/hal-05041467 ; The Seventeenth Workshop on Adaptive and Learning Agents, May 2025, Detroit (Michigan), United States |
| Publisher Information: |
CCSD |
| Publication Year: |
2025 |
| Collection: |
Université Grenoble Alpes: HAL |
| Subject Terms: |
Multi-Objective Optimization; Trust; Authentication; Internet of Things; Multi-Agent Systems; [INFO]Computer Science [cs] |
| Subject Geographic: |
Detroit (Michigan); United States |
| Description: |
International audience ; Multi-Agent Systems (MAS) deployed in the Internet of Things (IoT) face significant security challenges due to device heterogeneity and dynamic environments. Traditional authentication mechanisms often fail to address the various trade-offs that arise in resourceconstrained environments, which are critical for IoT systems. To address this, we propose a trust-based, multi-objective optimization framework for adaptive authentication factor selection in IoT. Our approach leverages the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the security, energy efficiency, and delay of the authentication factors. Trust is integrated into the optimization model as a guiding parameter to enable a more adaptive and contextaware selection process, ensuring that requirements and resource consumption are tailored to the specific context of each authentication instance. Simulation results demonstrate that our framework enhances security while optimizing resource consumption. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-05041467; https://hal.science/hal-05041467v1/document; https://hal.science/hal-05041467v1/file/Saideh_ALA_workshop_AAMAS_2025.pdf |
| Rights: |
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.37DB49FA |
| Database: |
BASE |