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.

翻译：网络入侵检测系统(NIDS)在计算机网络安全中扮演着重要角色。在多种检测机制中，基于异常的自动化检测性能显著优于其他方法。面对日益复杂且数量激增的攻击，处理海量数据成为开发基于异常的NIDS时公认的难题。然而，当前模型在所需精度和可靠性方面能否满足当今网络的需求？在本研究中，我们提出了一种结合机器学习和深度学习的新型混合模型，旨在提升检测率的同时确保可靠性。我们提出的方法通过结合SMOTE进行数据平衡和XGBoost进行特征选择，实现了高效的预处理。我们将所开发的方法与多种机器学习和深度学习算法进行比较，以寻找更适合集成到流水线中的高效算法。此外，我们基于一组经过基准测试的性能分析标准，选出了针对网络入侵的最有效模型。该方法在两个数据集KDDCUP'99和CIC-MalMem-2022上进行了测试，取得了优异的结果：在KDDCUP'99上准确率达99.99%，在CIC-MalMem-2022上准确率达100%，且未出现过拟合或第一类与第二类错误问题。