Using Hybrid Deep Learning Approach to Enhanced Network Intrusion Detection with Spatial-Temporal Feature Integration

Abstract

Intrusion Detection Systems (IDS) play a vital role in network security by detecting and preventing malicious activities. The network intrusion data is integrated into a vast number of common occurrences due to the dynamic and ever-changing networking environment. This results in a scarcity of training cases for models and detection outcomes, accompanied by a significant percentage of false detections. Our suggested Network-IDS addresses the issue of data imbalance by integrating Deep Learning Networks (DLN) via hybrid sampling. We begin by collecting out-of-the-ordinary samples from the majority and eliminating them using the Difficult-Set-Sampling-Technique method, which stands for Difficult-Set-Sampling-Technique (DSST). Next step is to increase the minority group's sample size using (DCGAN) means Deep-Convolutional-Generative-Adversarial-Networks. Step two involves building a model for a deep neural network to extract geographical features using DenseNet169, in addition, we utilize SAT-Net to capture features of temporal. This approach effectively represents the unique attributes of the dataset. Lastly, we deployed the EESNN to identify assault types. In addition to that, we conducted tests on the latest and most extensive intrusion datasets, the Telecommunications Network Internet of Things (ToN-IoT) dataset as well as the CICIDS2019 dataset, to verify of proposed approach. The outcome demonstrates that our recommended structure surpasses similar efforts in terms of accuracy, false alarm rate, recall, and precision. The findings indicate that our proposed system is superior to other attempts of a similar kind in terms of accuracy, false alarm rate, recall, and precision. We will provide a detailed explanation of this in the comparative section.