# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 6 Conclusions  
In order to effectively prevent the security risk of SSC malware, two novel models, BO-SVM and LSTM-BO-SVM, are proposed in this paper for the effective detection of malware in the SSC. The BO-SVM model is built on the basis of SVM, and its hyperparameters are optimized by the BO algorithm. The model is suitable for resource-limited environments. Then, the LSTM-BO-SVM model is constructed on the basis of the BO-SVM, and the sequence data are processed by the LSTM. The model is suitable for use in scenarios where the real-time requirements are low and accuracy is high. The experimental outcomes demonstrate that the BO-SVM model achieves an accuracy of 95.3% on the ClaMP dataset and 96.4% on the CICMalDroid-2020 dataset, outperforming the traditional SVM model. The LSTM-BO-SVM model, with the introduction of the LSTM network, further improves the accuracy with 98.2% and 98.6% on the ClaMP and CICMalDroid-2020 datasets, respectively, which demonstrates its potential for application in SSC malware detection tasks. Although the BO-SVM and LSTM-BO-SVM models are effective, other models, such as DL, have been reported to achieve better results in similar studies [21]. Therefore, future work can explore other more efficient and concise detection models. The current study is in the preliminary stage, which was conducted on a public dataset and not validated in a real environment. Future work can deeply analyze the applications of the model in real SSC attack scenarios. In addition, future work can explore more data sources or incorporate more diverse features to increase the robustness of the model and assess the impact of dataset bias on model performance. This paper investigates binary classification problems, which can be extended to multiclassification problems in future work. Moreover, as the size of the dataset grows and the feature dimensions increase, it will become crucial to explore more efficient hyperparameter optimization algorithms. This will not only improve the performance of the model, but also cope with more complex SSC environments in the future  
**Author Contributions:** Conceptualization, S.Z., H.L., and X.F. ; methodology, S.Z., H.L., and X.F. ; software, S.Z. and Y.J. ; validation, S.Z., H.L., and Y.J. ; formal analysis, S.Z., H.L., and X.F. ; investigation, S.Z. and Y.J. ; writing--original draft preparation, S.Z. and Y.J. ; writing--review and editing, S.Z. and Y.J. ; visualization, S.Z. ; supervision, H.L. and X.F. ; project administration, H.L. ; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant No. 62041211; the Basic Scientific Research Foundation Project of Colleges and Universities directly under the Inner Mongolia Autonomous Region under Grant No. BR22-14-05; the Inner Mongolia Autonomous Region Science and Technology Program under Grant No. 2022 YFHH0070; the China Ministry of Education industry-university cooperative education project Grant No. 2308062722 35841; the Natural Science Foundation project of the Inner Mongolia Autonomous Region Grant No. 2024 MS06002; and the Inner Mongolia Autonomous Region Graduate Research Innovation Project under Grant. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The ClaMP dataset is available at [[https://www.kaggle.com/datasets/saurabhishahane/classification-of-malwares](https://www.kaggle.com/datasets/saurabhishahane/classification-of-malwares)]([https://www.kaggle.com/datasets/saurabhishahane/classification-of-malwares](https://www.kaggle.com/datasets/saurabhishahane/classification-of-malwares)) (accessed on 7 May 2024). The CICMalDroid-2020 dataset is available at [[https://www.unb.ca/cic/datasets/maldroid-2020.html](https://www.unb.ca/cic/datasets/maldroid-2020.html)]([https://www.unb.ca/cic/datasets/maldroid-2020.html](https://www.unb.ca/cic/datasets/maldroid-2020.html)) (accessed on 7 May 2024).