What question did this study set out to answer?

This research aims to evaluate the effectiveness of BiLSTM models in detecting network anomalies using intrusion detection systems.

April 25, 2026

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

Key Points

This research aims to evaluate the effectiveness of BiLSTM models in detecting network anomalies using intrusion detection systems.
Utilized UNSW-NB15 dataset for testing the intrusion detection systems
Employed BiLSTM models to analyze the data and detect anomalies
Metrics such as accuracy and precision were assessed to evaluate detection performance
Achieved 96.7% accuracy for the intrusion detection system model
Demonstrated effective detection of intrusions using the proposed deep learning methods
Established a foundation for future advancements in network protection

Abstract

ABSTRACT 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.

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

Key Points

Abstract

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