# A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

arXiv: https://arxiv.org/abs/2212.04546 · HTML: https://mlsanity.com/paper/2212.04546/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 89 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost](https://mlsanity.com/paper/1908.01672/) (2019)
- [Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning](https://mlsanity.com/paper/2206.01088/) (2022)
- [Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques](https://mlsanity.com/paper/1711.02825/) (2017)
- [Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform](https://mlsanity.com/paper/2111.00601/) (2021)
- [Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues](https://mlsanity.com/paper/1912.03356/) (2019)
- [Security, Privacy and Trust: Cognitive Internet of Vehicles](https://mlsanity.com/paper/2104.12878/) (2021)
- [Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification](https://mlsanity.com/paper/2201.00584/) (2022)

## Built on by (most cited first, 12 of 12)

- [Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction](https://mlsanity.com/paper/2401.12262/) (2024)
- [Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture](https://mlsanity.com/paper/2311.16593/) (2023)
- [MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs](https://mlsanity.com/paper/2402.13277/) (2024)
- [Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search](https://mlsanity.com/paper/2402.14389/) (2024)
- [usfAD Based Effective Unknown Attack Detection Focused IDS Framework](https://mlsanity.com/paper/2403.11180/) (2024)
- [A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats](https://mlsanity.com/paper/2403.13010/) (2024)
- [Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency](https://mlsanity.com/paper/2509.17695/) (2025)
- [An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics](https://mlsanity.com/paper/2511.12743/) (2025)
- [Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans](https://mlsanity.com/paper/2403.05353/) (2024)
- [An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique](https://mlsanity.com/paper/2410.14489/) (2024)
- [Hybrid IDS Using Signature-Based and Anomaly-Based Detection](https://mlsanity.com/paper/2601.11998/) (2026)
- [Enhancing Multiclass Malware Classification in Resource-Constrained Environments](https://mlsanity.com/paper/2609.27950/) (2026)

## Abstract (arXiv)

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.