Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Machine learning (ML) classifiers have been widely deployed to detect Android malware,
but at the same time the application of ML classifiers also faces an emerging problem. The …

Android OS experiences a blazing popularity since the last few years. This predominant
platform has established itself not only in the mobile world but also in the Internet of Things …

Most existing Android malware detection and categorization techniques are static
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …

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

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

Building machine learning models of malware behavior is widely accepted as a panacea
towards effective malware classification. A crucial requirement for building sustainable …

Training pipelines for machine learning (ML) based malware classification often rely on
crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study …