A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

• URL: http://arxiv.org/abs/2212.04546v1
• Date: Thu, 8 Dec 2022 20:19:27 GMT
• Title: A Dependable Hybrid Machine Learning Model for Network Intrusion Detection
• Authors: Md. Alamin Talukder, Khondokar Fida Hasan, Md. Manowarul Islam, Md Ashraf Uddin, Arnisha Akhter, Mohammand Abu Yousuf, Fares Alharbi, Mohammad Ali Moni
• Abstract summary: We propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022.
• Score: 1.222622290392729
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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