2024

Conférence

SECRYPT 2024: 710-715

The integration of social networking concepts into the IoT environment has led to the Social Internet of Things
(SIoT) paradigm which enables connected devices and people to facilitate information sharing, interact, and
enable a variety of attractive applications. However, with this emerging paradigm, people feel cautious and
wary. They worry about violating their privacy and revealing their data. Without trustworthy mechanisms to
guarantee the reliability of user’s communications and interactions, the SIoT will not reach enough popularity
to be considered as a cutting-edge technology. Accordingly, trust management becomes a major challenge
to improve security and provide qualified services. Therefore, we overcome these issues through proposing
an adaptive trust management model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to
estimate the trust level of objects in the Social Internet of Things. We formalized and implemented a new trust
management model built ANFIS, to analyze different trust parameters, estimate the trust level of objects and
distinguish malicious behavior from benign behaviors. Experimentation made on a real data set proves the
performance and the resilience of our trust management model.

@inproceedings{DBLP:conf/secrypt/OuechtatiA24,
  author       = {Hamdi Ouechtati and
                  Nadia Ben Azzouna},
  editor       = {Sabrina De Capitani di Vimercati and
                  Pierangela Samarati},
  title        = {Towards an Adaptive Trust Management Model Based on {ANFIS} in the
                  SIoT},
  booktitle    = {Proceedings of the 21st International Conference on Security and Cryptography,
                  {SECRYPT} 2024, Dijon, France, July 8-10, 2024},
  pages        = {710--715},
  publisher    = {{SCITEPRESS}},
  year         = {2024},
  timestamp    = {Thu, 05 Sep 2024 14:21:37 +0200},
  biburl       = {https://dblp.org/rec/conf/secrypt/OuechtatiA24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}