HOW TO DETECT ANOMALIES IN NETWORK TRAFFIC USING RNN

dc.contributor.author	Ibrohimov Azizbek Ravshonbek o‘g‘li
dc.date.accessioned	2025-12-23T16:14:26Z
dc.date.issued	2025-10-27
dc.description.abstract	This study proposes a method for automatic anomaly detection using a recurrent neural network (LSTM RNN) based on network traffic metadata. The model examines temporal patterns of network flows and identifies deviations from normal situations as an attack. The results indicate that the model has high accuracy and stability.
dc.format	application/pdf
dc.identifier.uri	https://brightmindpublishing.com/index.php/ev/article/view/1533
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/3247
dc.language.iso	eng
dc.publisher	Bright Mind Publishing
dc.relation	https://brightmindpublishing.com/index.php/ev/article/view/1533/1559
dc.rights	https://creativecommons.org/licenses/by/4.0
dc.source	EduVision: Journal of Innovations in Pedagogy and Educational Advancements; Vol. 1 No. 10 (2025); 217-224
dc.source	3061-6972
dc.subject	LSTM, RNN, cybersecurity, anomaly detection, network traffic, machine learning, neural network, ISCX dataset.
dc.title	HOW TO DETECT ANOMALIES IN NETWORK TRAFFIC USING RNN
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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