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- W4313201183 abstract "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 in order 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." @default.
- W4313201183 created "2023-01-06" @default.
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- W4313201183 date "2023-02-01" @default.
- W4313201183 modified "2023-09-30" @default.
- W4313201183 title "A dependable hybrid machine learning model for network intrusion detection" @default.
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- W4313201183 doi "https://doi.org/10.1016/j.jisa.2022.103405" @default.
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