To cope with the increasing variability and sophistication of modern attacks, machine
learning has been widely adopted as a statistically-sound tool for malware detection …

Machine learning (ML) has been widely used for malware detection on different operating
systems, including Android. To keep up with malware's evolution, the detection models …

With smart phones being indispensable in people's everyday life, Android malware has
posed serious threats to their security, making its detection of utmost concern. To protect …

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these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

In this paper, we consider the relevance of timeline in the construction of datasets, to
highlight its impact on the performance of a machine learning-based malware detection …

In this paper we present LiM (" Less is More"), a malware classification framework that
leverages Federated Learning to detect and classify malicious apps in a privacy-respecting …

Malware remains a big threat to cyber security, calling for machine learning based malware
detection. While promising, such detectors are known to be vulnerable to evasion attacks …

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 …

Machine learning-based solutions have been successfully employed for the automatic
detection of malware on Android. However, machine learning models lack robustness to …

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 …