<p>The progression of information technology and cyberspace has driven economic growth and societal advancement, but it also introduces various complex and diverse security threats, such as network intrusions and attacks. The Network Intrusion Detection System (NIDS) is a crucial component in protecting against network intrusions, and so its ability to precisely identify different types of intrusions must be ensured. Over the years, IDS has evolved into a key defense strategy for combating network security breaches. The efficiency of IDS depends heavily on the methodologies employed to improve accuracy in identifying intrusions and reduce complexity during training and testing. This study presents a hybrid intrusion detection model that combines a machine learning (ML) classifier, such as LightGBM, with a stacked sparse autoencoder. The ML classifier is trained using the low‐dimensional features obtained from the input data. The proposed method minimizes the volume of data that needs to be processed and transmitted, resulting in increased efficiency and reduced storage requirements. This method reduces dataset features by over 80% without compromising detection performance. It not only decreases storage requirements but also alleviates network congestion when transmitting data to the cloud. The method achieved an overall accuracy of 99.24% with the NSL-KDD dataset and 100% with the Bot-IoT dataset. The outcomes of the experiment demonstrate that the model performs better in binary and multiclass classifications than standard ML methods and is effective for developing a high‐performance model for intrusion detection.</p>