Abstract. This paper presents a methodological framework for classifying network traffic anomalies, detailing the relationships between their sources, manifestation areas, and characteristic features. Optimized detection schemes for anomalies and misuse based on threshold-based, statistical, and machine learning methods are discussed. A formal model for detecting and classifying anomalous events in distributed networks is proposed, ensuring high detection accuracy and adaptive parameter tuning.
| Mualliflar | Abdulhamidova, Nilufar |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-10-08 |
| Son | 3 |
| Betlar | 159-163 |
| Til | Rus |
Ключевые слова: сетевая аномалия, классификация, детекция, машинное обучение, пороговые методы, адаптивная модель., : Deepfake, Generative Adversarial Networks (GANs), Cybersecurity, Deepfake Detection., Kalit so‘zlar: tarmoq anomaliyasi, tasniflash, aniqlash, mashinali o‘qitish, chegaraviy usullar, moslashuvchan model
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