Evaluation of Deep CNN-BiLSTM Model on Diverse Datasets

In the field of network intrusion detection, a wide range of machine learning and deep learning models have been developed, including the successful CNN-BiLSTM [1] model, which has shown promising results on datasets such as NSL-KDD and UNSW-NB15. Building upon these achievements, we conduct an evaluation of the CNN-BiLSTM model on the ToNIoT and CICIDS2018 datasets to provide a comprehensive and objective assessment of its effectiveness in network attack detection. By testing the model's performance on these datasets, we aim to further validate its efficacy and establish its applicability in real-world scenarios.