Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3769 2019 2019 Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Shivanthana Shivanthana Department of Artificial Intelligence and Data Science, Nandha Engineering College, Erode, India Manicka Raja. M. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Lalitha Krishnasamy Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India Karthik. R. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India R. Venkatesan 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 2025 13 27 10.54216/JCIM.160202 https://www.americaspg.com/articleinfo/2/show/3769