There is a significant intrusion detection security problem facing today’s cyber civilization. Network intrusion attacks have increased dramatically in recent years, which has led to serious issues regarding security and privacy. As a result of technical advancement, cyber-security threats are becoming more sophisticated, making it hard for present detection techniques to manage the issue. Therefore, developing a clever and effective networking detection system for intrusions would be essential to fixing this issue. In this study, we developed an intelligent intrusion detection system that leverages deep learning techniques, namely Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), to detect various networking threats. We employed a CNN and DNN ensemble model, which gives us very accurate results. The acquired data undergoes analysis and pre-processing prior to being utilized for model training and testing. Additionally, we contrasted the results of our suggested solution and assessed the suggested solution’s effectiveness through a number of evaluation matrices so as to choose the best model for the computer network intrusion detection system.