## 4 Discussion  
The results presented in this paper demonstrate the effectiveness of the proposed GRU-GAN model. The model consistently outperformed the BiLSTM and BiGRU models across multiple datasets, highlighting its robustness and generalizability. Several factors contribute to the superior performance of the GRU-GAN model. First, the adversarial training process of GANs enables the model to learn more robust and generalizable features from API call sequences, allowing it to capture subtle patterns that distinguish malicious from benign behavior, even in complex or previously unseen samples. Second, the integration of GRUs enhances the model's ability to process sequential data, which is crucial for effectively analyzing API call sequences. In addition to achieving higher accuracy, the GRU-GAN model demonstrated improved computational efficiency compared to the BiLSTM and BiGRU models. Across the datasets, the GRU-GAN consistently required less training time, testing time, and memory usage. This efficiency is particularly noteworthy given the common assumption that more complex models like GANs would incur higher computational costs. The reduced resource requirements of the GRU-GAN model have significant implications for real-world applications, enabling more frequent model updates, faster threat detection, and deployment in resource-constrained environments. These findings underscore the potential of our proposed model as a reliable tool for malware detection, especially in dynamic and evolving threat landscapes where new, previously unseen patterns may emerge. The confusion matrix results and prediction performance visualizations further emphasize the effectiveness of our model. While the BiLSTM and BiGRU models showed increasing overlap between malicious and benign classifications across the datasets, the GRU-GAN maintained a clear separation, minimizing both false positives and false negatives. This ability to distinguish between malicious and benign samples with high accuracy is crucial in real-world malware detection systems, where misclassifications can lead to security vulnerabilities or unnecessary alerts. The resilience of the GRU-GAN model to dataset variations suggests its potential effectiveness in detecting novel or evolving malware threats. As malware authors continually develop new techniques to evade detection, a model that can maintain high accuracy across diverse datasets is particularly valuable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } & \multicolumn{4}{c}{**Computational Efficiency**} \\ \cline{3-5}  & & **Training Time** & **Testing Time** &
\begin{tabular}{c} **Memory** \\ **Usage** \\ **(MB)** \\ \end{tabular} \\ \hline [PERSON] et al. (2023) [2] & 84.0 & 0.40 & \(\times\) & \(\times\) \\ [PERSON] et al. (2023) [29] & 99.07 & 35.721 & 0.177 & \(\times\) \\ [PERSON] et al. (2022) [4] & 99.3 & 6.673 & 0.011 & \(\times\) \\ [PERSON] et al. (2020) [14] & 98.50 & 2569.2 & \(\times\) & \(\times\) \\ [PERSON] et al. (2015) [31] & 99.80 & \(\times\) & \(\times\) & \(\times\) \\
**Our Proposed Model** & **99.99** & **57** & **7.2** & **75** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed model with existing research. The application of GRU-GAN models in malware detection has shown significant promise, offering advanced capabilities for analyzing API call sequences and detecting malicious patterns. However, despite these advances, several challenges remain unresolved, particularly in the areas of generalization, adversarial robustness, and dynamic adaptation. As malware continues to evolve rapidly and attackers develop more sophisticated techniques, improving the performance and reliability of GRU-GAN models becomes increasingly critical. One of the key open issues is the challenge of improving generalization in GRU-GAN models for malware detection.