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.
| Mualliflar | Turg'unaliyev, Shohruz |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-03-27 |
| Son | 1 |
| Betlar | 356-363 |
| Til | Rus |
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).
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