Generative adversarial and transformer network synergy for robust intrusion detection in IoT environments

Pardis Sadatian Moghaddam; Ali Vaziri; Sarvenaz Sadat Khatami; F. Hernando-Galego; Diego Martín

doi:10.3390/fi17060258

Publication record · 2025

Generative adversarial and transformer network synergy for robust intrusion detection in IoT environments

Authors: Pardis Sadatian Moghaddam · Ali Vaziri · Sarvenaz Sadat Khatami · F. Hernando-Galego · Diego Martín
Year: 2025
Publisher: MDPI
Journal or publication: Future Internet
Google Scholar citations: 38

Journal indicators

CiteScore 2025 (Scopus)10

Best quartileQ1

Top 10% in any categoryNo

Positions by category

Computer Networks and Communications65/568 · Q1 · percentile 88

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JCR (Clarivate): Not verified: the supplied ranking file is Scopus CiteScore, not JCR.

Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).

The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.

Research summary

A hybrid Transformer-GAN intrusion detector models contextual traffic dependencies while generating synthetic examples to reduce class imbalance. Multi-class experiments on CIC-IoT-2023 and TON_IoT assess robustness, efficiency and detection of diverse IoT attacks.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.3390/fi17060258 ↗ · Open-access version ↗ · View Google Scholar record ↗