Conventional Machine Learning-Based Android Malware Detectors [* Cross-Reference *]

Specifically, we present the dataset used in their performance evaluation. We delve into the feature set adopted by the different approaches and describe how they are embedded in vector spaces. Furthermore, we conduct an in-depth exploration of the classification process, including the ML algorithms used, their hyper-parameters, and the methodology employed in the evaluation.

Additionally, we provide examples showcasing the performance effectiveness of the studied approaches. Lastly, we discuss the limitations and challenges of ML-based malware detectors that need to be overcome to advance the research field. ***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to doi.org/10.1007/978-3-031-66245-4_7 for the original and latest version of the dataset and data downloads***