Unsupervised anomaly detection in network traffic using autoencoders: an empirical study on the nsl-kdd dataset

Turg'unaliyev, Shohruz

Al-Farg'oniy avlodlari · 2026-yil

Annotatsiya

The scarcity of labeled data remains a fundamental limitation of supervised learning models. This study proposes an autoencoder-based unsupervised approach for detecting anomalies in network traffic. Experiments conducted on the NSL-KDD dataset demonstrate that the model, trained exclusively on normal traffic, achieved an AUC-ROC of 96.13% and Precision of 96.25%. The detection of 40.22% of zero-day attacks without any signatures confirms the superiority of autoencoders over traditional signature-based systems. A hybrid approach is recommended as the most effective strategy.

Maqola ma’lumotlari
MualliflarTurg'unaliyev, Shohruz
JurnalAl-Farg'oniy avlodlari
NashriyotТАТУ Фарғона филиали
ISSN2181-4252
Nashr sanasi2026-03-27
Son1
Betlar356-363
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

Autoencoder, network security, anomaly detection, unsupervised learning, zero-day attacks, NSL-KDD, deep learning, Intrusion Detection System (IDS)., Автоэнкодер, сетевая безопасность, обнаружение аномалий, обучение без учителя, атаки нулевого дня, NSL-KDD, глубокое обучение, система обнаружения вторжений (IDS)., Autoencoder, tarmoq xavfsizligi, anomaliyalarni aniqlash, nazoratsiz o‘qitish, zero-day hujumlar, NSL-KDD, chuqur o'qitish, Intruziyalarni aniqlash tizimi (IDS).

Ilmiy soha

Texnika

Iqtibos keltirish

APATurg'unaliyev, Shohruz (2026). UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET. Al-Farg'oniy avlodlari, (1), 356-363. https://al-fargoniy.uz/index.php/journal/article/view/1086
GOST (OAK)Turg'unaliyev, Shohruz UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET // Al-Farg'oniy avlodlari. – 2026. – № 1. – P. 356-363.
MLATurg'unaliyev, Shohruz. “UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET.” Al-Farg'oniy avlodlari, (1), 356-363, 2026. https://al-fargoniy.uz/index.php/journal/article/view/1086
ChicagoTurg'unaliyev, Shohruz. “UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET.” Al-Farg'oniy avlodlari, (1), 356-363 (2026). https://al-fargoniy.uz/index.php/journal/article/view/1086
HarvardTurg'unaliyev, Shohruz (2026) ‘UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET’, Al-Farg'oniy avlodlari, (1), 356-363. https://al-fargoniy.uz/index.php/journal/article/view/1086
BibTeX
@article{turgunaliyevshohruz2026,
  title = {UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING AUTOENCODERS: AN EMPIRICAL STUDY ON THE NSL-KDD DATASET},
  author = {Turg'unaliyev, Shohruz},
  journal = {Al-Farg'oniy avlodlari},
  year = {2026},
  number = {1},
  pages = {356-363},
  url = {https://al-fargoniy.uz/index.php/journal/article/view/1086}
}

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