H. J. Zhu, Z. H. You, Z. X. Zhu, W. L. Shi, X. Chen et al., DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model, Neurocomputing, vol.272, pp.638-646, 2018.

N. Milosevic, A. Dehghantanha, and K. R. Choo, Machine learning aided Android malware classification, Comput. Electr. Eng, vol.61, pp.266-274, 2017.

Y. Aafer, W. Du, and H. Yin, DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android, Secur. Priv. Commun. Networks, vol.127, pp.86-103, 2013.

L. Li, Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting, IEEE Trans. Inf. Forensics Secur, vol.12, issue.6, pp.1269-1284, 2017.

W. Yang, D. Kong, T. Xie, and C. A. Gunter, Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps, pp.288-302, 2017.

A. Abraham, R. Andriatsimandefitra, A. Brunelat, J. F. Lalande, V. Viet-triem et al., GroddDroid: A gorilla for triggering malicious behaviors, 2015 10th International Conference on Malicious and Unwanted Software, pp.119-127, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01201743

M. Leslous, V. Viet-triem, J. Tong, T. Lalande, and . Genet, GPFinder: Tracking the Invisible in Android Malware, 12th International Conference on Malicious and Unwanted Software, pp.39-46, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01584989

K. Allix, T. F. Bissyandeé, J. Klein, and Y. Le-traon, AndroZoo: Collecting Millions of Android Apps for the Research Community. Mining Software Repositories (MSR), p.2016