Malware is malicious software designed to compromise computer systems, and poses a significant threat to businesses, with potential repercussions ranging from financial losses to damaged reputations and eroded customer trust. To address this challenge, we propose a hybrid deep learning approach that combines the power of Long Short Term Memory (LSTM) and Gated Recurrent Units (GRUs), both of which are models in the Recurrent Neural Network (RNN) family. Our research focuses on assessing the potential improvements achieved by this hybrid approach, leveraging a benchmark dataset known as NSL-KDD+. This dataset offers a temporal dimension and encompasses a diverse array of malware samples and network traffic scenarios for comprehensive testing and evaluation. We employ a range of performance metrics, including Accuracy, Precision, F1 Score, Mean Absolute Error (MAE), and others, to comprehensively gauge the effectiveness of our proposed approach.