Modeling Intrusion Detection for the Landscape Design Software Procreate from Data Layer

In today's digital landscape, network security faces escalating challenges with the proliferation of cyber threats. Intrusion detection is a central defense mechanism against these threats, requiring constant innovation and refinement. This study presents a novel approach to modeling intrusion detection specifically tailored to Procreate, a landscape design software, with a focus on the data layer. Leveraging machine learning and deep learning techniques, proposed methodology encompasses comprehensive steps including feature selection, data processing, and model implementation. Through experimentation and comparison with the traditional methods, the proposed model demonstrates superior performance in terms of accuracy, error rate, recall rate, precision, F-measure, AUC, and computational efficiency. The results highlight the effectiveness of proposed approach in enhancing network security for Procreate users and provide valuable insights for future research directions.