Russel, M. O. F. K., Rahman, S. S. M. M., & Alazab, M. AndroShow: A Large Scale Investigation to Identify the Pattern of Obfuscated Android Malware. In Machine Intelligence and Big Data Analytics for Cybersecurity Applications (pp. 191-216). Springer, Cham.

Russel, M. O. F. K., Rahman, S. S. M. M., & Islam, T. (2020, July). A Large-Scale Investigation to Identify the Pattern of App Component in Obfuscated Android Malwares. In International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (pp. 513-526). Springer, Singapore.

Russel, M. O. F. K., Rahman, S. S. M. M., & Islam, T. (2020, February). A Large-Scale Investigation to Identify the Pattern of Permissions in Obfuscated Android Malwares. In International Conference on Cyber Security and Computer Science (pp. 85-97). Springer, Cham.

Rahman, S. S. M. M., & Saha, S. K. (2018, December). StackDroid: Evaluation of a multi-level approach for detecting the malware on android using stacked generalization. In International Conference on Recent Trends in Image Processing and Pattern Recognition (pp. 611-623). Springer, Singapore.

Islam, T., Rahman, S. S. M. M., Hasan, M. A., Rahaman, A. S. M. M., & Jabiullah, M. I. (2020). Evaluation of N-Gram Based Multi-Layer Approach to Detect Malware in Android. Procedia Computer Science, 171, 1074-1082.

Rana, M. S., Rahman, S. S. M. M., & Sung, A. H. (2018, September). Evaluation of tree based machine learning classifiers for android malware detection. In International Conference on Computational Collective Intelligence (pp. 377-385). Springer, Cham.