AI-Driven Threat Detection Models: Integrating Deep Learning for Enhanced Cybersecurity Against Emerging Network Vulnerabilities

Modern cybersecurity requires fast-detecting threat prevention systems which identify new network weaknesses through highly precise mechanisms. This study develops an AI-based framework which combines deep learning features with data augmentation methods alongside hybrid classification strategies for strengthening cybersecurity defences. The methodology uses data augmentation techniques which include GAN sand SMOTE for creating synthetic attack patterns and balancing dataset distribution to enhance model predictive accuracy. For extracting spatial-temporal network traffic patterns, the decompressive process takes autoencoders and convolutional neural networks (CNNs) as deep learning methods. A model utilizing both LSTM and Transformer-based architectures within a hybrid structure enables efficient detection of recognized as well as new and unexpected security threats. Benchmark datasets including NSL-KDD and CICIDS2017 showed that SHAP improves detection quality and decreases false alarms together with better transparency for debugging purposes according to experimental testing results. The suggested method enhances proactive response capabilities in cybersecurity defence by delivering an adaptable solution that combats developing cyber threats on a large scale.