DSEM-NIDS: Enhanced Network Intrusion Detection System Using Deep Stacking Ensemble Model

The need to deploy a network intrusion detection system (NIDS) is essential and has become increasingly necessary for every network, regardless whether it is wired, wireless, or hybrid, and its purpose is commercial, medical, defense, or social. Since the amount of data transfer over the Internet increases every year, using a single model as an IDS to secure the network cannot be considered enough as it may have many problems like high bias or high variance, which lead to high rates of false negatives and false positives. In this paper, we propose an ensemble learning-based NIDS (DSEM-NIDS); this system is a deep-stacking model with a nested structure that has the ability to score a high performance with low false positive and low false negative rates. Four datasets are used as a benchmark to evaluate the proposed model: The 5G-NIDD, UNR-IDD, N-BaIoT, and NSL-KDD datasets. The results show that the proposed deep stacking model is robust, has good scalability, has the ability to distinguish between classes, and has the flexibility to adapt to different input data. It also performs better than other used models.