Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems.
- DOI
- 10.3390/s24020479
- Published
- 2024 Jan 12
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s24020479,
title = {Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems.},
author = {Medina-Arco JG and Magán-Carrión R and Rodríguez-Gómez RA and García-Teodoro P},
year = {2024},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s24020479},
url = {https://doi.org/10.3390/s24020479}
}RIS
TY - JOUR TI - Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems. AU - Medina-Arco JG AU - Magán-Carrión R AU - Rodríguez-Gómez RA AU - García-Teodoro P PY - 2024 JO - Sensors (Basel, Switzerland) DO - 10.3390/s24020479 UR - https://doi.org/10.3390/s24020479 ER -
APA
JG, M., R, M., RA, R., & P, G. (2024). Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s24020479
Source records
- pubmed · retrieved 2026-09-26T04:36:18.357Z