An Ensemble Approach of CNN and GRU Models for Network and Server Intrusion

This research paper delves into the realm of cybersecurity by proposing an innovative ensemble model that blends Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for network and server intrusion detection. CNNs excel in spatial feature extraction, particularly suited for analyzing network traffic patterns, while GRUs are adept at capturing temporal dependencies within sequential data. The integration of these models aims to leverage their complementary strengths, enhancing the overall robustness of intrusion detection systems. Through comprehensive training and evaluation on diverse datasets, the ensemble model showcases promising results, demonstrating improved accuracy and resilience against adversarial attacks compared to standalone CNN and GRU models. In addition to making a significant contribution to the rapidly developing field of deep learning in cybersecurity, this research offers practical implications for the development of more secure network and server environments in the face of dynamic cyber threats.