Impact of Resampling techniques in Deep Learning based Intrusion Detection: A Comparative Study on NSL-KDD and UNSW-NB15
Year: 2025
Type: conference-paper
Abstract: This study examines the effectiveness of a specific set of data resampling techniques within a deep learning framework to enhance network intrusion detection system (NIDS) which is a critical aspect of cybersecurity. We evaluate a CNN-BLSTM model for attack classification on two widely used benchmark NIDS datasets (NSL-KDD and UNSW-NB15) that exhibit varying degrees of class imbalance which can undermine the detection of rare yet critical cyberattacks. Our approach integrates Adaptive Synthetic (ADASYN) oversampling both independently and in combination with undersampling methods such as Random UnderSampling (RUS), TomekLinks and One-Sided Selection (OSS) to address this imbalance. The results demonstrate that for the NSL-KDD dataset which suffers from severe imbalance, ADASYN coupled with OSS significantly improves classification accuracy. In contrast, for the UNSW-NB15 dataset, with relatively milder imbalances, the deep learning model performs comparably well without extensive resampling. Beyond the standard metrics, we also conduct additional evaluations through class-wise accuracy analysis and permutation-based feature importance to better understand the impact of resampling on minority class performance and feature relevance. These findings underscore the importance of tailoring resampling strategies to the specific class distributions within NIDS datasets to optimize deep learning performance in cybersecurity applications.
Source: Procedia Computer Science
Authors: Nisha Dangol, Amir Eaman, Elhadi Shakshuki, Esteve Hassan
Institution: Acadia University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416640364