Android malware detection is a serious issue for mobile security. Recent machine learning-
based research could achieve high accuracy. However, there are far more unlabeled …

As in other cybersecurity areas, machine learning (ML) techniques have emerged as a
promising solution to detect Android malware. In this sense, many proposals employing a …

With the extensive use of Android applications, malware growth has been increasing
drastically. The high popularity of Android devices has motivated malware developers to …

Year after year, mobile malware attacks grow in both sophistication and diffusion. As the
open source Android platform continues to dominate the market, malware writers consider it …

Research on Android malware detection based on Machine learning has been prolific in
recent years. In this paper, we show, through a large-scale evaluation of four state-of-the-art …

Android malware has continued to grow in volume and complexity posing significant threats
to the security of mobile devices and the services they enable. This has prompted increasing …

The increasing proliferation of Androidbased devices, which currently dominate the market
with a staggering 72% global market share, has made them a prime target for attackers …

As Android is a popular a mobile operating system, Android malware is on the rise, which
poses a great threat to user privacy and security. Considering the poor detection effects of …

Android malware attacks are tremendously increasing, and evasion techniques become
more and more effective. For this reason, it is necessary to continuously improve the …

Permissions and the network traffic features are the widely used attributes in static and
dynamic Android malware detection respectively. However, static permissions cannot detect …