  Network intrusion detection systems (NIDSs) play an important role in
computer network security. There are several detection mechanisms where
anomaly-based automated detection outperforms others significantly. Amid the
sophistication and growing number of attacks, dealing with large amounts of
data is a recognized issue in the development of anomaly-based NIDS. However,
do current models meet the needs of today's networks in terms of required
accuracy and dependability? In this research, we propose a new hybrid model
that combines machine learning and deep learning to increase detection rates
while securing dependability. Our proposed method ensures efficient
pre-processing by combining SMOTE for data balancing and XGBoost for feature
selection. We compared our developed method to various machine learning and
deep learning algorithms to find a more efficient algorithm to implement in the
pipeline. Furthermore, we chose the most effective model for network intrusion
based on a set of benchmarked performance analysis criteria. Our method
produces excellent results when tested on two datasets, KDDCUP'99 and
CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and
CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.
