# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 5 Conclusions and Future Work  
Malware has grown to be a common problem in computer security. Therefore, it is crucial to have an effective malware detection method to detect malware precisely. While many malware detection techniques have decent performance, some still need to improve in semantic information analysis. This work introduces a new approach to dynamic malware detection on the basis of behavioral features. First, a vectorized representation is performed on the basis of API call sequences, which is trained with the Skip-Gram model to acquire the contextual semantic information of APIs. Then, the API call names are encoded by analyzing the semantic information of the name structure and the statistical features. Finally, the embedded API sequences are fed into the CNNs-BiGRU network to train the detector for malware. Multiple sets of experiments demonstrate that this proposed model superior to all the proposed baselines and can effectively achieve malware detection, reaching an accuracy of 0.9828 and an F1-Score of 0.9827.  
There are some potential problems with our framework, which may limit its practical application. For example, encoding API names manually suffers from a lack of flexibility. In order to improve the ability to learn semantic information with a high degree of flexibility, there is an urgent need to work on developing a more automated way of encoding API  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Structure** & **Semantic** \\ \hline [PERSON] et al. [46] & 0.9660 & 0.9569 & 0.9775 & 0.9671 & ✗ & ✗ \\ [PERSON] et al. [45] & 0.9668 & 0.9763 & 0.9575 & 0.9668 & ✗ & ✓ \\ [PERSON] et al. [47] & 0.9685 & 0.9720 & 0.9642 & 0.9681 & ✗ & ✗ \\ [PERSON] et al. [48] & 0.9738 & 0.9774 & 0.9712 & 0.9743 & ✓ & ✗ \\ Proposed Model & 0.9828 & 0.9870 & 0.9789 & 0.9827 & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Various architectures’ performance comparison based on the embedding model.  
names. This will help the system understand the semantic meaning of APIs more accurately. In addition, many recent studies have shown that API parameters can effectively improve malware detection. In the next step, we plan to analyze API parameters and further modify our approach to analyze malware more comprehensively. Some new malware variants emerge as new loopholes are discovered, which lead to concept drift, a phenomenon where the incoming test distribution deviates from the original training distribution [56]. That will result in the classifier's performance to start degrading over time. In the future, we will modify our model to develop a more robust feature space and adapt to the drift.  
Conceptualization, S.Y. and D.Z.; methodology, Y.Z.; software, Y.Z.; validation, S.Y. and L.X.; formal analysis, L.X. and X.L.; writing--original draft preparation, Y.Z.; writing--review and editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Shandong Province (ZR2021 MF132 and ZR2020 YQ06), in part by the National Natural Science Foundation of China (62172244), in part by the National Major Program for Technological Innovation 2030-New Generation Artificial Intelligence (2020 AAA0107700), in part by the Taishan Scholars Program (tsqm202211210), in part of by the Graduate Education and Teaching Reform Research Project of Shandong Province (SDY)G21177), in part by the Education Reform Project of Qlu University of Technology (2021 yb63).  
Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from Github and are available from the authors with the permission of Github.  
The authors declare no conflict of interest.