IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15.

Mohamed N, Taherdoost H

DOI: 10.3389/fdata.2026.1885965
Published: 2026
Container: Frontiers in big data
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fdata.2026.1885965,
  title = {IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15.},
  author = {Mohamed N and Taherdoost H},
  year = {2026},
  journal = {Frontiers in big data},
  doi = {10.3389/fdata.2026.1885965},
  url = {https://doi.org/10.3389/fdata.2026.1885965}
}

RIS

TY  - JOUR
TI  - IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15.
AU  - Mohamed N
AU  - Taherdoost H
PY  - 2026
JO  - Frontiers in big data
DO  - 10.3389/fdata.2026.1885965
UR  - https://doi.org/10.3389/fdata.2026.1885965
ER  -

APA

N, M., & H, T. (2026). IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15.. Frontiers in big data. https://doi.org/10.3389/fdata.2026.1885965

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pubmed · retrieved 2026-09-26T06:23:32.067Z