Enhanced Intrusion Detection Using Conditional-Tabular-Generative-Adversarial-Network-Augmented Data and a Convolutional Neural Network: A Robust Approach to Addressing Imbalanced Cybersecurity Datasets

Shridhar Allagi (), Toralkar Pawan and Wai Yie Leong
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Shridhar Allagi: Department of Computer Science and Engineering, KLE Institute of Technology, Hubballi 580030, India
Toralkar Pawan: Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi 580027, India
Wai Yie Leong: Faculty of Engineering and Quantity Surveying, INTI International University, Nilai 71800, Malaysia

Mathematics, 2025, vol. 13, issue 12, 1-22

Abstract: Intrusion prevention and classification are common in the research field of cyber security. Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance the dataset. SMOTE in turn fails to address the constraints associated with the dataset, such as diverse data types, preserving the data distribution, capturing non-linear relationships, and preserving oversampling noise. The novelty of this work is in addressing the issues associated with data distribution and SMOTE by employing Conditional Tabular Generative Adversarial Networks (CTGANs) on NSL_KDD and UNSW_NB15 datasets. The balanced input corpus is fed into the CNN model to predict the intrusion. The CNN model involves two convolution layers, max-pooling, ReLU as the activation layer, and a dense layer. The proposed work employs measures such as accuracy, recall, precision, specificity and F1-score for measuring the model performance. The study shows that CTGAN improves the intrusion detection rate. This research highlights the high-quality synthetic samples generated by CTGAN that significantly enhance CNN-based intrusion detection performance on imbalance datasets. This demonstrates the potential for deploying GAN-based oversampling techniques in real-world cybersecurity systems to improve detection accuracy and reduce false negatives.

Keywords: intrusion detection; synthetic minority oversampling technique (SMOTE); conditional tabular generative adversarial network (CTGAN); convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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