aspg-2-3769-1791951497 20261014041817 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2025 16 2 Shivanthana S. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Manicka Raja M. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Lalitha Krishnasamy Department of Artificial Intelligence and Data Science, Nandha Engineering College, Erode, India Karthik R. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India R. Venkatesan Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Network security faces significant challenges due to the increasing sophistication of cyber threats and the inherent class imbalance in intrusion detection datasets. To address this issue, a hybrid Boundary Equilibrium Generative Adversarial Network (BEGFAN) and Vector Quantization Variational Autoencoder (VQVAE) framework, termed BVQVAE, is proposed for Network Intrusion Detection Systems (NIDS). The framework involves preprocessing, feature extraction, and class balancing to enhance classification accuracy. Missing values are imputed, categorical features are label-encoded, and numerical attributes are normalized to ensure a structured dataset. BEGAN generates synthetic samples to mitigate class imbalance, while VQVAE extracts essential features using an encoder with quantization and a decoder for network traffic reconstruction. The model is evaluated on NSL-KDD and UNSW-NB15 datasets, achieving 82.56% accuracy, with precision, recall, G-mean, and F1-score of 86.53%, 87.65%, 86.21%, and 87.08%, respectively. 2025 13 27 3769 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.160202 https://americaspg.com/journal/2/article/3769 https://americaspg.com/storage/81749567899.pdf