With the rise of sophisticated cyberattacks and the advent of complex and diverse technological systems, traditional methods of intrusion detection have become insufficient. The inability to prevent intrusions poses a severe threat to the credibility of security services, which may result in the compromise of data confidentiality, integrity, and availability. To address this challenge, research has proposed the use of Artificial Intelligence (AI) and deep learning (DL) models to enhance the effectiveness of intrusion detection. In this study, we present an Intrusion Detection System (IDS) that utilizes attention-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. The attention mechanism of the model allows for the identification of significant features in network traffic data for more precise predictions. Using the benchmark dataset UNSW-NB15, we validate the robustness and effectiveness of our model, achieving a detection rate of over 95%. Our results emphasize the robustness and effectiveness of the proposed system, demonstrating the immense potential of AI and DL models in bolstering intrusion detection.