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Intelligent Network Behavior Anomaly Detection Using LSTM-Based Deep Learning Models
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DOI：10.1002/itl2.70279.png)
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Abstract
En 中文
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
Journal
I
IF:
0.5
Papers:
182
Citations:
423
Organization
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