# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
## 5 Conclusions and Future Work  
In conclusion, this research aimed to develop and evaluate a malware detection method based on a hybrid model that learns from API call sequences. Our approach relied on a secondary dataset consisting of API call sequences, which we used to train the proposed model and obtain the initial weights for the neural network. A comparative analysis of our method with the widely used [PERSON] model demonstrated the effectiveness of our proposed approach in addressing the challenges of both balanced and imbalanced datasets, achieving a comparable or even superior performance in terms of accuracy, loss values, and training time. Figure 5: Loss comparison with imbalanced data. (**a**) The proposed model loss with imbalanced data. (**b**) Xavier model loss with imbalanced data. Figure 6: Accuracy comparison with imbalanced data. (**a**) The proposed model accuracy with imbalanced data. (**b**) Xavier model accuracy with balanced data. The comparative evaluation between the proposed method and the widely adopted Xavier 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.The experiments in this research allowed crucial insights into the influence of weight initialization methods on neural network performance, specifically in malware detection. Our results highlighted the potential of our hybrid model to enhance the precision and efficiency in malware detection, along with the significance of addressing class imbalances and optimizing the training procedure. In subsequent studies, we plan to broaden the proposed model to encompass malware detection for different operating systems, thereby increasing the applicability and pertinence of our strategy. Furthermore, we aim to explore the performance of our hybrid model when trained with limited data and compare it to outcomes attained using conventional weight initialization methods in neural networks with more extensive datasets. This comparison will yield a different interpretation of the trade-offs associated with different weight initialization techniques, assisting us in pinpointing the most effective methods for designing and implementing neural networks for malware detection across various operating systems and scenarios. It is also vital to recognize the importance of generating a balanced dataset and effectively addressing class imbalance, as this ensures the model's ability to accurately differentiate between distinct classes, such as malicious and benign software. This aspect is particularly critical in cybersecurity applications, where false alarms and misclassifications can have serious repercussions. By investigating diverse techniques for handling class imbalance, researchers can create more resilient and adaptable models that perform efficiently across a wide array of real-world challenges. Our research has made a valuable contribution to the field of neural networks and malware detection by proposing a novel hybrid model and demonstrating its effectiveness compared to the widely-used Xavier model. The findings of this study have important implications for the development of more robust and efficient neural networks, as well as for the broader understanding of weight initialization techniques in deep learning. To sum up, this research proposed a novel malware detection method based on a hybrid model learned from API call sequences and demonstrated its effectiveness and potential advantages over traditional weight initialization methods in neural networks. By expanding the proposed model to cover other operating systems and investigating its performance with different data sizes, we aim to further refine and enhance the applicability and relevance of our approach. This, in turn, will contribute to the ongoing efforts to improve the accuracy, efficiency, and versatility of neural networks for malware detection and other cybersecurity applications.