mlsanityData to 7 Oct 2026

A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

arXiv 2212.04546 ↗First posted 8 Dec 2022cs.CR cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 9 of its 89 references that are in the map. First posted on arXiv 8 Dec 2022.

Builds on

9 of 89 references.

References · 9

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  3. 2019Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoostcited by 15
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  9. 2022Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classificationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 2.

12 papers.

  1. 2024Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extractioncited by 16
  2. 2023Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecturecited by 8
  3. 2024MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNscited by 5
  4. 2024Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Searchcited by 4
  5. 2024usfAD Based Effective Unknown Attack Detection Focused IDS Frameworkcited by 2
  6. 2024A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreatscited by 1
  7. 2025Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiencycited by 1
  8. 2025An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metricscited by 1
  9. 2024Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scanscited by 0
  10. 2024An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Techniquecited by 0
  11. 2026Hybrid IDS Using Signature-Based and Anomaly-Based Detectioncited by 0
  12. 2026Enhancing Multiclass Malware Classification in Resource-Constrained Environmentscited by 0