Impacto de ataques de evasão e eficácia da defesa baseada em treinamento adversário em detectores de malware

• Gabriel H. N. Espindola da Silva UEL
• Gilberto Fernandes Junior UEL
• Bruno Bogaz Zarpelão UEL

DOI: https://doi.org/10.5753/sbseg.2024.240800

Resumo

Algoritmos de aprendizado de máquina (AM) podem ajudar na detecção de programas maliciosos, ou malwares, ao identificar padrões de comportamento deles. No entanto, os modelos de AM são vulneráveis a ataques de aprendizado de máquina adversário (AMA), permitindo que malwares sejam classificados como benignos. Este trabalho investiga o impacto dos ataques dFGSM (Deterministic Fast Gradient Sign Method), rFGSM (Randomic Fast Gradient Sign Method), BGA (Bit Gradient Ascent), BCA (Bit Coordinate Ascent) e Grosse contra detectores de malware e a eficácia da defesa baseada em treinamento adversário. Os resultados mostraram que ataques de alta intensidade (dFGSM, rFGSM, BGA) impactam significativamente a acurácia dos detectores, mesmo com treinamento adversário.

Referências

Al-Dujaili, A., Huang, A., Hemberg, E., and O’Reilly, U.-M. (2018). Adversarial deep learning for robust detection of binary encoded malware. In 2018 IEEE Security and Privacy Workshops (SPW), pages 76–82. IEEE.

Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., and Siemens, C. (2014). Drebin: Effective and explainable detection of android malware in your pocket. In Ndss, volume 14, pages 23–26.

Aslan, Ö. A. and Samet, R. (2020). A comprehensive review on malware detection approaches. IEEE Access, 8:6249–6271.

Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P. (2016). Adversarial perturbations against deep neural networks for malware classification. arXiv preprint arXiv:1606.04435.

Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P. (2017). Adversarial examples for malware detection. In European symposium on research in computer security, pages 62–79. Springer.

Kolosnjaji, B., Demontis, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., and Roli, F. (2018). Adversarial malware binaries: Evading deep learning for malware detection in executables. In 2018 26th European signal processing conference (EUSIPCO), pages 533–537. IEEE.

Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A. (2017). Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, pages 506–519.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Pierazzi, F., Pendlebury, F., Cortellazzi, J., and Cavallaro, L. (2020). Intriguing properties of adversarial ml attacks in the problem space. In 2020 IEEE symposium on security and privacy (SP), pages 1332–1349. IEEE.

Rosenberg, I., Shabtai, A., Rokach, L., and Elovici, Y. (2018). Generic black-box end-to-end attack against state of the art api call based malware classifiers. In International Symposium on Research in Attacks, Intrusions, and Defenses, pages 490–510. Springer.

Shaukat, K., Luo, S., and Varadharajan, V. (2022). A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks. Engineering Applications of Artificial Intelligence, 116:105461.