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

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.