## 3 Results
### Performance Evaluation
#### 3.1.3. The Computational Complexity of BiLSTM, BiGRU, and GRU-GAN Models  
The computational efficiency of deep learning models is a key factor in their practical application, particularly in malware detection, where real-time performance is often crucial. Table 5 and Figure 13 provide a detailed overview of the computational resources utilized by the BiLSTM, BiGRU, and GRU-GAN models on datasets 1 and 2, offering valuable insights into their efficiency in terms of training time, testing time, and memory usage. We have intentionally excluded dataset 3 from this computational evaluation, as it was utilized exclusively for testing the performance of the models rather than for training or comprehensive resource analysis. On dataset 1, the BiLSTM model demonstrated moderate computational requirements, with a training time of 80 s, a testing time of 12.7 s, and a memory usage of 120.3 MB. The BiGRU model showed improved efficiency, requiring 69 s for training, 8.4 s for testing, and 90 MB of memory. The GRU-GAN model, on the other hand, outperformed both in terms of computational efficiency, completing training in 57 s, testing in 7.2 s, and utilizing only 75 MB of memory. This performance indicates that despite its more complex architecture, the GRU-GAN model is more computationally efficient than the other models evaluated. Dataset 2, being potentially more complex, requires increased computational resources across all models. The BiLSTM model's training time increased to 127 s, with a testing time of 14.8 s and memory usage of 147 MB. The BiGRU model showed a similar trend, requiring 112 s for training, 11.5 s for testing, and 104 MB of memory. However, the GRU-GAN model demonstrated superior efficiency, completing training in 98 s, testing in 9.2 s, and utilizing 90 MB of memory. This consistent performance across datasets highlights the scalability and efficiency capabilities of the GRU-GAN model in handling larger and more complex datasets.  
Figure 12: Prediction performance of the BiLSTM, BiGRU, and GRU-GAN models on dataset 3.  
Figure 13 visually supports these findings, clearly illustrating the computational advantage of the GRU-GAN model across both datasets. The graph demonstrates that the GRU-GAN consistently required less training time, testing time, and memory compared to both the BiLSTM and BiGRU models. This visual representation underscores the scalability of the GRU-GAN model, as it maintained its computational edge even as the dataset complexity increased. Conventional reasoning might suggest that the additional complexity of the GAN framework would lead to increased computational demands. However, the results indicate that the GRU-GAN model not only achieved higher accuracy, as discussed in previous sections, but also did so with lower computational overhead. This efficiency can be attributed to the ability of GAN to learn more effective representations of the data, requiring fewer iterations to achieve optimal performance.  
The reduced training and testing times of the GRU-GAN model have significant implications for real-world applications in malware detection. Faster training times allow for more frequent model updates, enabling the system to adapt quickly to new malware variants. The shorter testing times are important for real-time threat detection, where rapid identification of potential malware is essential to prevent security breaches. Moreover, the lower memory usage of the GRU-GAN model is advantageous for deployment in resource-constrained environments or for integration into existing security systems without requiring significant hardware upgrades. This efficiency could facilitate the wider adoption of advanced deep learning-based malware detection systems across various computing platforms.