Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection
Computing Sciences and Computer Engineering
Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.
Vietnam Journal of Computer Science
Rana, M. S.,
Sung, A. H.
(2020). Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection. Vietnam Journal of Computer Science, 7(2), 145-159.
Available at: https://aquila.usm.edu/fac_pubs/17402