A Dependable Hybrid Machine Learning Model for Network Intrusion Detection
This work addresses the need for more reliable intrusion detection in computer network security, though it appears incremental as it combines existing techniques like SMOTE and XGBoost.
The researchers tackled the problem of improving accuracy and dependability in network intrusion detection systems by proposing a hybrid machine learning and deep learning model, achieving 99.99% accuracy on KDDCUP'99 and 100% on CIC-MalMem-2022 datasets with no overfitting or error issues.
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.