Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation.
- DOI
- 10.3390/s26030757
- Published
- 2026 Jan 23
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26030757,
title = {Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation.},
author = {Ioannou I and Vassiliou V},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26030757},
url = {https://doi.org/10.3390/s26030757}
}RIS
TY - JOUR TI - Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation. AU - Ioannou I AU - Vassiliou V PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26030757 UR - https://doi.org/10.3390/s26030757 ER -
APA
I, I., & V, V. (2026). Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26030757
Source records
- pubmed · retrieved 2026-09-26T19:05:19.594Z