Abstract
In this paper, we analyze current methods to distinguish malware from benign software using Machine Learning (ML) and feature engineering techniques that have been implemented in recent years. Moreover, we build a new dataset based on API calls gathered from software analysis, conforming more than 30000 samples belonging to malware as well as benign software. Finally, we test this dataset with an existing model that achieves accuracy rates close to 97% with a different, smaller dataset, identifying interesting results that can open new research opportunities in this field.
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References
Alazab, M., Venkatraman, S., Watters, P., Alazab, M., Alazab, A.: Cybercrime: the case of obfuscated malware. In: Global Security, Safety and Sustainability & E-Democracy, pp. 204–211 (2012)
Yuan, X.: PhD Forum: deep learning-based real-time malware detection with multi-stage analysis. In: 2017 IEEE International Conference On Smart Computing (SMARTCOMP), pp. 1–2 (2017)
Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp. 712–717 (2017)
Singh, J., Singh, J.: A survey on machine learning-based malware detection in executable files. J. Syst. Archit. 112, 101861 (2021). https://www.sciencedirect.com/science/article/pii/S1383762120301442
Sami, A., Yadegari, B., Rahimi, H., Peiravian, N., Hashemi, S., Hamze, A.: Malware detection based on mining API calls. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1020–1025 (2010). https://doi.org/10.1145/1774088.1774303
Trinius, P., Willems, C., Holz, T., Rieck, K.: A malware instruction set for behavior-based analysis. None (2009). https://madoc.bib.uni-mannheim.de/2579/
Rabadi, D., Teo, S.: Advanced windows methods on malware detection and classification. In: Annual Computer Security Applications Conference, pp. 54–68 (2020). https://doi.org/10.1145/3427228.3427242
Gamage, S., Samarabandu, J.: Deep learning methods in network intrusion detection: a survey and an objective comparison. J. Netw. Comput. Appl. 169, 102767 (2020). https://www.sciencedirect.com/science/article/pii/S1084804520302411
Zhang, Z., Qi, P., Wang, W.: Dynamic malware analysis with feature engineering and feature learning. Proc. AAAI Conf. Artif. Intell. 34, 1210–1217 (2020). https://ojs.aaai.org/index.php/AAAI/article/view/5474
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., Attenberg, J.: Feature hashing for large scale multitask learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1113–1120 (2009). https://doi.org/10.1145/1553374.1553516
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference On Machine Learning, vol. 37, pp. 448–456 (2015). https://proceedings.mlr.press/v37/ioffe15.html
Dauphin, Y., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 933–941 (2017). https://proceedings.mlr.press/v70/dauphin17a.html
Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing [review article]. IEEE Comput. Intell. Magaz. 13, 55–75 (2018)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation. In: Encyclopedia of Database Systems, pp. 1–7 (2016)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006). https://www.sciencedirect.com/science/article/pii/S016786550500303X. ROC Analysis in Pattern Recognition
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Torres, M., Álvarez, R., Cazorla, M. (2023). Improving Malware Detection with a Novel Dataset Based on API Calls. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_28
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DOI: https://doi.org/10.1007/978-3-031-18050-7_28
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