A Deep Learning Method for Obfuscated Android Malware Detection
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62217-5_13
Reference11 articles.
1. Dhanya K. A.: Obfuscated malware detection in iot android applications using markov images and cnn. IEEE Syst. J., 1–11 (2023). https://doi.org/10.1109/JSYST.2023.3238678
2. Aboaoja, F., Zainal, A., Ghaleb, F., Al-rimy, B., Eisa, T., Elnour, A.: Malware detection issues, challenges, and future directions: a survey. Appli. Sci. 12, 8482 (08 2022). https://doi.org/10.3390/app12178482
3. Aonzo, S., Georgiu, G.C., Verderame, L., Merlo, A.: Obfuscapk: an open-source black-box obfuscation tool for android apps. SoftwareX 11, 100403 (2020). https://doi.org/10.1016/j.softx.2020.100403, https://www.sciencedirect.com/science/article/pii/S2352711019302791
4. Mahdavifar, S., Abdul kadir, A.F., Fatemi, R., Alhadidi, D., Ghorbani, A.: Dynamic android malware category classification using semi-supervised deep learning, pp. 515–522 (Aug 2020). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094
5. Mahdavifar, S., Alhadidi, D., Ghorbani, A.: Effective and efficient hybrid android malware classification using pseudo-label stacked auto-encoder. J. Netw. Syst. Manag. 30 (2022). https://doi.org/10.1007/s10922-021-09634-4
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