# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
## 4 Results and Discussion
### Imbalance Dataset  
In this particular dataset, we employed the entire data population, without applying any resampling techniques. This choice was grounded in the understanding that neural networks generally yield a higher accuracy when working with larger amounts of data. Moreover, imbalanced classes are a common feature in most real-world problems. Consequently, we utilized the complete dataset, to capitalize on all available information, despite any potential imbalance issues. It is crucial, however, to acknowledge that using the full dataset might introduce a bias towards the majority class, leading to a diminished accuracy for the minority class. This occurrence, referred to as overfitting, can result in inadequate generalization when encountering new data. As observed in Table 3, the recall and f1-score of both models diverged significantly between the malware and benign classes. Both models exhibited comparable performance in terms of accuracy, but the proposed model outperformed [PERSON]'s model in this aspect. Specifically, the proposed model reached an accuracy of 0.98, whereas [PERSON]'s model attained an accuracy of 0.92. The findings presented in this analysis emphasize the importance of considering the impact of class imbalances on model performance, particularly regarding metrics such as recall and f1-score. While using the entire dataset can indeed improve the overall accuracy, it is vital to be mindful of the trade-offs involved, as well as the potential for overfitting. By exploring various techniques for addressing class imbalances, researchers can develop more robust and versatile models that perform effectively across a diverse range of real-world problems. The comparative evaluation between the proposed method and the widely adopted [PERSON] model illustrated the efficacy of our suggested method in tackling the challenges posed by both balanced and imbalanced datasets, delivering comparable or even better performance in terms of accuracy, loss values, and training duration. In the end, one of the most notable observations was that the loss values of the two models were almost indistinguishable. As depicted in Figure 5, the proposed model commenced with a comparable loss value after the training process. This finding implies that it might be possible to reduce the training time in the future, without compromising the high level of accuracy achieved by the model. By examining the similarities in loss values between the proposed model and [PERSON]'s model, we can gain insights into the potential for optimizing the training process of neural networks. A reduced training time, while maintaining high accuracy, could lead to more efficient deployment of these models in real-world applications. Further research and experimentation in this area could uncover new techniques and approaches that enhance the overall performance and efficiency of neural networks, enabling them to better tackle complex and diverse problems across various domains. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & _Accuracy_ & _Best Loss_ & _Time (s)_ & _Malware_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline \multirow{2}{*}{\(LR\)} & \multirow{2}{*}{0.98} & \multirow{2}{*}{0.97} & \multirow{2}{*}{0.40} & 0 & 0.83 & 0.38 & 0.52 \\ \cline{5-8}  & & & & 1 & 0.98 & 1.00 & 0.99 \\ \hline \multirow{2}{*}{\(SLRW\)} & \multirow{2}{*}{0.98} & \multirow{2}{*}{0.10} & \multirow{2}{*}{157.00} & 0 & 0.72 & 0.46 & 0.56 \\ \cline{5-8}  & & & & 1 & 0.99 & 1.00 & 0.99 \\ \hline \multirow{2}{*}{\(SXW\)} & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.10} & \multirow{2}{*}{154.90} & 0 & 0.19 & 0.64 & 0.29 \\ \cline{5-8}  & & & & 1 & 0.99 & 0.93 & 0.96 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation scores for malware and benign classes with the imbalanced dataset with logistic regression (LR), sequential with logistic regression weights (SLRW), and sequential with Xavier weight (SXW) models.