## 5 Conclusions  
This paper introduced a novel hybrid deep learning model that combines Gated Recurrent Units (GRUs) and Generative Adversarial Networks (GANs) to enhance the detection of malware by analyzing API call sequences. The GRU-GAN model demonstrated exceptional performance across multiple datasets, achieving a remarkable accuracy of 99.99%, significantly surpassing other models like BiLSTM and BiGRU. In addition to its high accuracy, the GRU-GAN model exhibited robust generalization capabilities, with a precision of 99.7%, a recall of 99.8%, and an AUC of 99% on challenging test datasets. Furthermore, it maintained low false-positive and false-negative rates, which are critical for minimizing misclassification in real-world malware detection scenarios. This study made some key contributions to the field of malware detection. Firstly, we propose a novel hybrid model that leverages the sequential processing capabilities of GRUs with the generative potential of GANs. This combination allows for more effective detection of sophisticated and evasive malware patterns, improving detection accuracy while maintaining computational efficiency. Secondly, the GRU-GAN model was evaluated against the BiLSTM and BiGRU models using three distinct datasets, demonstrating its superior performance in terms of both accuracy and computational efficiency. It consistently outperformed the other models, showing excellent results in terms of precision, recall, and F1 score, proving its robustness in identifying previously unseen malware samples. Lastly, the GRU-GAN model not only achieved high accuracy but also proved to be computationally efficient. It required significantly less training time (57 s) and memory usage (75 MB) compared to other models, making it highly suitable for real-world applications that demand low resource consumption and quick detection times. While the current research demonstrated the effectiveness of the GRU-GAN model across three datasets, future work could greatly benefit from incorporating more diverse and comprehensive data sources. One key direction would be to scale the datasets to include a broader spectrum of malware families, including rare or emerging types. By doing so, the model would encounter a wider variety of behaviors during training, thus improving its ability to generalize and detect previously unseen malware. Additionally, collecting datasets from different sectors, such as health care or finance, could offer valuable insights into how malware targeting specific industries operates. This would allow for the development of more specialized detection models. Integrating real-time data, where malware samples are continually fed into the system as they are discovered, would also enable the model to stay current and better adapt to the fast-evolving nature of cyber threats. Though API call sequences provide critical insights into malware behavior, relying solely on these data could limit the detection capabilities of the model. Future research could explore the integration of additional features, such as file signatures and network traffic data. For example, malware often manipulates file structures or system files, and incorporating file signatures could allow for the detection of these modifications. Network traffic data are also a valuable source of information, as many types of malware establish communication with command-and-control (C2) servers or exfiltrate sensitive data. Monitoring network traffic, alongside API calls, could give a more comprehensive picture of malware activities. System-level metadata, such as file entropy or memory usage patterns, could also be integrated to uncover subtle signs of malicious behavior that API calls alone might not reveal. By enriching the feature set, future models could achieve even higher detection accuracy and resilience against sophisticated threats.