DroidAutoML: uma Ferramenta de AutoML para o Domínio de Detecção de Malwares Android
Resumo
Neste trabalho apresentamos a DroidAutoML, uma ferramenta de AutoML especializada para o domínio de detecção de malwares Android. A partir de um dataset de entrada e um pipeline de dados de quatro estágios, a DroidAutoML possibilita a geração automática de modelos preditivos otimizados. Os resultados iniciais indicam que a ferramenta é capaz de gerar modelos muito bons, chegando a 95% de recall.
Palavras-chave:
AutoML, Detecção de Malware Android, Dataset, Ferramenta
Referências
Cai, L., Li, Y., and Xiong, Z. (2021). JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters. Computers & Security, 100:102086.
Karmaker (“Santu”), S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., and Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. ACM Comp. Sur., 54(8).
Lee, D. J.-L. and Macke, S. (2020). A human-in-the-loop perspective on automl: Milestones and the road ahead. IEEE Data Engineering Bulletin.
Martin, R. C., Grenning, J., Brown, S., Henney, K., and Gorman, J. (2017). Clean architecture: a craftsman’s guide to software structure and design. Number 31. Prentice Hall.
Moutaz, A. (2020). Automated malware detection in mobile app stores based on robust feature generation. Electronics, 9:435.
Sagi, O. and Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1249.
Sharma, T. and Rattan, D. (2021). Malicious application detection in android—a systematic literature review. Computer Science Review, 40:100373.
Shi, X., Wong, Y. D., Chai, C., and Li, M. Z.-F. (2021). An automated machine learning (automl) method of risk prediction for decision-making of autonomous vehicles. IEEE TITS, 22(11):7145.
Siqueira, G., Rodrigues, G., Kreutz, D., and Feitosa, E. (2021). QuickAutoML: Uma ferramenta para treinamento automatizado de modelos de aprendizado de máquina. In WRSeg21.
Sun, L., Li, Z., Yan, Q., Srisa-an, W., and Pan, Y. (2016). SigPID: significant permission identification for android malware detection. In 11th MALWARE, pages 1–8.
Xin, D., Wu, E. Y., Lee, D. J.-L., Salehi, N., and Parameswaran, A. (2021). Whither automl - understanding the role of automation in machine learning workflows. In Proceedings of the CHI.
Yan, C., Zhang, Y., Zhang, Q., Yang, Y., Jiang, X., Yang, Y., and Wang, B. (2022). Privacy-preserving online automl for domain-specific face detection. In IEEE CVF, pages 4134–4144.
Karmaker (“Santu”), S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., and Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. ACM Comp. Sur., 54(8).
Lee, D. J.-L. and Macke, S. (2020). A human-in-the-loop perspective on automl: Milestones and the road ahead. IEEE Data Engineering Bulletin.
Martin, R. C., Grenning, J., Brown, S., Henney, K., and Gorman, J. (2017). Clean architecture: a craftsman’s guide to software structure and design. Number 31. Prentice Hall.
Moutaz, A. (2020). Automated malware detection in mobile app stores based on robust feature generation. Electronics, 9:435.
Sagi, O. and Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1249.
Sharma, T. and Rattan, D. (2021). Malicious application detection in android—a systematic literature review. Computer Science Review, 40:100373.
Shi, X., Wong, Y. D., Chai, C., and Li, M. Z.-F. (2021). An automated machine learning (automl) method of risk prediction for decision-making of autonomous vehicles. IEEE TITS, 22(11):7145.
Siqueira, G., Rodrigues, G., Kreutz, D., and Feitosa, E. (2021). QuickAutoML: Uma ferramenta para treinamento automatizado de modelos de aprendizado de máquina. In WRSeg21.
Sun, L., Li, Z., Yan, Q., Srisa-an, W., and Pan, Y. (2016). SigPID: significant permission identification for android malware detection. In 11th MALWARE, pages 1–8.
Xin, D., Wu, E. Y., Lee, D. J.-L., Salehi, N., and Parameswaran, A. (2021). Whither automl - understanding the role of automation in machine learning workflows. In Proceedings of the CHI.
Yan, C., Zhang, Y., Zhang, Q., Yang, Y., Jiang, X., Yang, Y., and Wang, B. (2022). Privacy-preserving online automl for domain-specific face detection. In IEEE CVF, pages 4134–4144.
Publicado
12/09/2022
Como Citar
ASSOLIN, Joner; KREUTZ, Diego; SIQUEIRA, Guilherme; ROCHA, Vanderson; MIERS, Charles; MANSILHA, Rodrigo; FEITOSA, Eduardo.
DroidAutoML: uma Ferramenta de AutoML para o Domínio de Detecção de Malwares Android. In: SALÃO DE FERRAMENTAS - SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 22. , 2022, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 135-142.
DOI: https://doi.org/10.5753/sbseg_estendido.2022.227037.
