Intelligent Network Behavior Anomaly Detection Using LSTM-Based Deep Learning Models

Muna Alsallal

doi:10.1002/itl2.70279

Intelligent Network Behavior Anomaly Detection Using LSTM-Based Deep Learning Models

2026-04-23

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Albrge, Basma Salim Bazel

DOI：10.1002/itl2.70279

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Abstract

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Cyber threats are becoming increasingly common in today's digital landscape. The importance of IDS is evident when it comes to preventing harm to a company. Combating these dangers has been achieved using a variety of methods. ML models, and in particular BiLSTM models, were used to test the effectiveness of trustworthy intrusion detection systems using UNSW-NB15 data. Experiments have shown 96.7% accuracy for the IDS model. Using the suggested method, accuracy, precision, and other metrics are evaluated to show that intrusions are accurately detected. The results of this study provide a solid foundation for future research and advancements aimed at improving the overall protection of networks and safeguarding sensitive data.

Keywords:

bi-LSTM

deep learning

IDS

ML

network localization

reliability

Intelligent Network Behavior Anomaly Detection Using LSTM-Based Deep Learning Models

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