Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods

arXiv:2301.12778v2 Announce Type: replace
Abstract: The popularity of Android means it is a common target for malware. Over the years, various studies have found that machine learning models can effectively discriminate malware from benign applications. However, as the operating system evolves, so does malware, bringing into question the findings of these previous studies, many of which report very high accuracies using small, outdated, and often imbalanced datasets. In this paper, we reimplement 18 representative past works and reevaluate them using …

abstract android applications arxiv comparison cs.cr cs.lg detection experimental feature found however importance machine machine learning machine learning models malware malware detection operating system studies survey type