Ferramentas de extração de características para análise estática de aplicativos Android
Resumo
Neste estudo, investigamos algumas das ferramentas utilizadas para a extração de características estáticas de aplicações Android, que são frequentemente utilizadas por métodos de detecção de malwares. Demonstramos que as ferramentas podem diferir quanto ao quantitativo e a apresentação dos dados extraídos, o que acaba condicionando sua aplicabilidade às necessidades específicas de cada projeto.Referências
Alazab, M., Alazab, M., Shalaginov, A., Mesleh, A., and Awajan, A. (2020). Intelligent mobile malware detection using permission requests and api calls. Future Generation Computer Systems, 107:509–521.
Bibi, I., Akhunzada, A., Malik, J., Iqbal, J., Musaddiq, A., and Kim, S. (2020). A dynamic dl-driven architecture to combat sophisticated android malware. IEEE Access, 8:129600–129612.
Dharmalingam, V. P. and Palanisamy, V. (2021). A novel permission ranking system for android malware detection—the permission grader. Journal of Ambient Intelligence and Humanized Computing, 12(5):5071–5081.
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., and Rajarajan, M. (2014). Android security: a survey of issues, malware penetration, and defenses. IEEE communications surveys & tutorials, 17(2):998–1022.
Hijawi, W., Alqatawna, J., Al-Zoubi, A. M., Hassonah, M. A., and Faris, H. (2021). Android botnet detection using machine learning models based on a comprehensive static analysis approach. Journal of Information Security and Applications, 58:102735.
Pan, Y., Ge, X., Fang, C., and Fan, Y. (2020). A systematic literature review of android malware detection using static analysis. IEEE Access, 8:116363–116379.
Schmicker, R., Breitinger, F., and Baggili, I. (2018). Androparse-an android feature extraction framework and dataset. In International Conference on Digital Forensics and Cyber Crime, pages 66–88. Springer.
Sharma, T. and Rattan, D. (2021). Malicious application detection in android — a systematic literature review. Computer Science Review, 40:100373.
Wang, L., He, R., Wang, H., Xia, P., Li, Y., Wu, L., Zhou, Y., Luo, X., Sui, Y., Guo, Y., and Xu, G. (2021). Beyond the virus: A first look at coronavirus-themed mobile malware.
Wang, W., Zhao, M., Gao, Z., Xu, G., Xian, H., Li, Y., and Zhang, X. (2019). Constructing features for detecting android malicious applications: Issues, taxonomy and directions. IEEE Access, 7:67602– 67631.
Zhang, X., Zhang, Y., Zhong, M., Ding, D., Cao, Y., Zhang, Y., Zhang, M., and Yang, M. (2020). Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware. In Proceedings of the 2020 ACM SIGSAC, CCS ’20, page 757–770, New York, NY, USA. ACM.
Bibi, I., Akhunzada, A., Malik, J., Iqbal, J., Musaddiq, A., and Kim, S. (2020). A dynamic dl-driven architecture to combat sophisticated android malware. IEEE Access, 8:129600–129612.
Dharmalingam, V. P. and Palanisamy, V. (2021). A novel permission ranking system for android malware detection—the permission grader. Journal of Ambient Intelligence and Humanized Computing, 12(5):5071–5081.
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., and Rajarajan, M. (2014). Android security: a survey of issues, malware penetration, and defenses. IEEE communications surveys & tutorials, 17(2):998–1022.
Hijawi, W., Alqatawna, J., Al-Zoubi, A. M., Hassonah, M. A., and Faris, H. (2021). Android botnet detection using machine learning models based on a comprehensive static analysis approach. Journal of Information Security and Applications, 58:102735.
Pan, Y., Ge, X., Fang, C., and Fan, Y. (2020). A systematic literature review of android malware detection using static analysis. IEEE Access, 8:116363–116379.
Schmicker, R., Breitinger, F., and Baggili, I. (2018). Androparse-an android feature extraction framework and dataset. In International Conference on Digital Forensics and Cyber Crime, pages 66–88. Springer.
Sharma, T. and Rattan, D. (2021). Malicious application detection in android — a systematic literature review. Computer Science Review, 40:100373.
Wang, L., He, R., Wang, H., Xia, P., Li, Y., Wu, L., Zhou, Y., Luo, X., Sui, Y., Guo, Y., and Xu, G. (2021). Beyond the virus: A first look at coronavirus-themed mobile malware.
Wang, W., Zhao, M., Gao, Z., Xu, G., Xian, H., Li, Y., and Zhang, X. (2019). Constructing features for detecting android malicious applications: Issues, taxonomy and directions. IEEE Access, 7:67602– 67631.
Zhang, X., Zhang, Y., Zhong, M., Ding, D., Cao, Y., Zhang, Y., Zhang, M., and Yang, M. (2020). Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware. In Proceedings of the 2020 ACM SIGSAC, CCS ’20, page 757–770, New York, NY, USA. ACM.
Publicado
27/10/2021
Como Citar
PONTES, Jonas; COSTA, Estevão; ROCHA, Vanderson; NEVES, Nicolas; FEITOSA, Eduardo; ASSOLIN, Joner; KREUTZ, Diego.
Ferramentas de extração de características para análise estática de aplicativos Android. In: ESCOLA REGIONAL DE REDES DE COMPUTADORES (ERRC), 19. , 2021, Charqueadas/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 37-42.
DOI: https://doi.org/10.5753/errc.2021.18539.
