Classification using machine learning has been a major class of defense solutions against
malware. Yet in the presence of a large and growing number of learning-based malware …

Machine learning–based classification dominates current malware detection approaches for
Android. However, due to the evolution of both the Android platform and its user apps …

Approaches to Android malware detection built on supervised learning are commonly
subject to frequent retraining, or the trained classifier may fail to detect newly emerged or …

To address the issue of malware detection through large sets of applications, researchers
have recently started to investigate the capabilities of machine-learning techniques for …

Machine-learning models have been recently used for detecting malicious Android
applications, reporting impressive performances on benchmark datasets, even when trained …

In the past few years, both the industry and the academic communities have developed
several approaches to detect malicious Android apps. State-of-the-art research approaches …

The topic of mobile malware detection on the Android platform has attracted significant
attention over the last several years. However, while much research has been conducted …

Machine learning (ML)-based Android malware detection has been one of the most popular
research topics in the mobile security community. An increasing number of research studies …

A large number of research studies have been focused on detecting Android malware in
recent years. As a result, a reliable and large-scale malware dataset is essential to build …

Given the frequent changes in the Android framework and the continuous evolution of
Android malware, it is challenging to detect malware over time in an effective and scalable …