Enhanced Intrusion Detection Using Conditional-Tabular-Generative-Adversarial-Network-Augmented Data and a Convolutional Neural Network: A Robust Approach to Addressing Imbalanced Cybersecurity Datasets
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 con...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/12/1923 |
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| Summary: | 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. |
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| ISSN: | 2227-7390 |