Abstract

Network intrusion detection systems face high-dimensional traffic, which degrades accuracy and raises computational costs. We evaluate Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and a deep autoencoder on the UNSW-NB15 dataset using eight classifiers. RFE delivers peak accuracy (92.5%) with minimal variability. PCA restores near-baseline accuracy while preserving 95% variance with minor tuning. The autoencoder yields nonlinear embeddings but demands extensive training and trails classical methods. These findings guide the selection of reduction strategies under accuracy requirements and resource constraints.

Recommended Citation

Wosiak, A., Świąder, K., Woźniak, R. & Niewiadomski, A. (2025). Enhancing Network Intrusion Detection through Data Dimensionality Reduction Using Classical and Deep Learning ApproachesIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.44

Paper Type

Poster

DOI

10.62036/ISD.2025.44

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Enhancing Network Intrusion Detection through Data Dimensionality Reduction Using Classical and Deep Learning Approaches

Network intrusion detection systems face high-dimensional traffic, which degrades accuracy and raises computational costs. We evaluate Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and a deep autoencoder on the UNSW-NB15 dataset using eight classifiers. RFE delivers peak accuracy (92.5%) with minimal variability. PCA restores near-baseline accuracy while preserving 95% variance with minor tuning. The autoencoder yields nonlinear embeddings but demands extensive training and trails classical methods. These findings guide the selection of reduction strategies under accuracy requirements and resource constraints.