IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i12p1923-d1675701.html
   My bibliography  Save this article

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

Author

Listed:
  • Shridhar Allagi

    (Department of Computer Science and Engineering, KLE Institute of Technology, Hubballi 580030, India
    Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi 580027, India)

  • Toralkar Pawan

    (Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi 580027, India
    Computer Science and Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Madanapalle 517325, India)

  • Wai Yie Leong

    (Faculty of Engineering and Quantity Surveying, INTI International University, Nilai 71800, Malaysia)

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.

Suggested Citation

  • Shridhar Allagi & Toralkar Pawan & Wai Yie Leong, 2025. "Enhanced Intrusion Detection Using Conditional-Tabular-Generative-Adversarial-Network-Augmented Data and a Convolutional Neural Network: A Robust Approach to Addressing Imbalanced Cybersecurity Datase," Mathematics, MDPI, vol. 13(12), pages 1-22, June.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:1923-:d:1675701
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/12/1923/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/12/1923/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:1923-:d:1675701. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.