Enhancing Network Security: Deep Learning-Powered Intrusion Detection with Data Engineering Techniques

The research work focuses on improving network security by integrating Deep Learning techniques, data engineering methodologies, and adaptive defensive measures. The context emphasizes the growing intricacy of cyber risks and the need for sophisticated Intrusion Detection System. The methods used include optimizing DL architectures via pruning and quantization, including ensemble learning, implementing dynamic data augmentation, and improving model interpretability. The observed findings show significant improvements in the performance of intrusion detection. The accuracy of the model climbed from 92.5% to 96.1%, the precision improved from 89.7% to 93.8%, the recall went from 94.2% to 97.2%, and the F1-score enhanced from 91.8% to 95.5%. These metrics indicate the system's capacity to identify and counteract incursions with greater precision and effectiveness. It enhances the methodical and efficient method of detecting intrusions, specifically targeting the ever-changing nature of cyber threats. The integrated architecture greatly improves the robustness and flexibility of IDS, allowing for the implementation of proactive defensive measures in contemporary network settings.