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Intrusion detection systems (IDS) are crucial to network security by identifying and stopping harmful actions. The network intrusion data are blended into an enormous quantity of typical instances as an outcome of the dynamic and time-varying networking surroundings. This leads to a lack of instances for training models and detection outcomes with a high false detection rate. In response to the data imbalance issue, we provide a network intrusion detection (NIDS) technique that combines deep networks and hybrid sampling. With the help of the Difficult Set Sampling Technique (DSSTE) algorithm, we first reduce the noise samples in the majority category before applying Deep Convolutional Generative Adversarial Networks (DCGANs) to boost the minority sample size. Additionally, we create a deep network model using DenseNet169 to extract spatial characteristics and Self Attention-based Transformer (SAT-Net) to extract temporal features. This technique accurately extracts the distinctive characteristics of the data. Finally, we employed Enhanced Elman Spike Neural Network (EESNN) for classifying the attack categories. We undertake experiments on the more recent and comprehensive intrusion datasets BOT-IOT, ToN-IoT, and CICIDS2019 in order to validate the suggested technique. Results indicate that our suggested system outperforms comparable works regarding accuracy, false alarm rate, recall, and precision.

EESNN: Hybrid Deep Learning Empowered Spatial-Temporal Features for Network Intrusion Detection System