The rise in popularity of the Android platform has resulted in an explosion of malware threats
targeting it. As both Android malware and the operating system itself constantly evolve, it is …

As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

Android users are constantly threatened by an increasing number of malicious applications
(apps), generically called malware. Malware constitutes a serious threat to user privacy …

Mobile devices are especially vulnerable nowadays to malware attacks, thanks to the
current trend of increased app downloads. Despite the significant security and privacy …

Machine learning methods can detect Android malware with very high accuracy. However,
these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

Most existing malicious Android app detection approaches rely on manually selected
detection heuristics, features, and models. In this paper, we describe a new, complementary …

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 …

The Android ecosystem has witnessed a surge in malware, which not only puts mobile
devices at risk but also increases the burden on malware analysts assessing and …

The drastic increase of Android malware has led to a strong interest in developing methods
to automate the malware analysis process. Existing automated Android malware detection …

As Android malware grows and evolves, deep learning has been introduced into malware
detection, resulting in great effectiveness. Recent work is considering hybrid models and …