Orthogonal variance-based feature selection for intrusion detection systems
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Abstract (arXiv)
In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 25 Oct 2021.
Builds on
4 of 21 references.
References · 4
- 2020Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detectioncited by 17
- 2018Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environmentscited by 6
- 2020Gamma distribution-based sampling for imbalanced datacited by 6
- 2021Feature selection for intrusion detection systemscited by 3
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Citing papers per year: 2025: 1.
1 paper.