The ever-increasing growth of online services and smart connectivity of devices have posed
the threat of malware to computer system, android-based smart phones, Internet of Things …

The extensive research in machine learning based Android malware detection showcases
high-performance metrics through a wide range of proposed solutions. Consequently, this …

Malware attacks continue to evolve, making detection challenging for traditional static and
dynamic analysis techniques. On the other hand, memory analysis provides valuable …

Ransomware attacks continue to evolve, with increasingly complex cryptographic payloads
designed to evade detection and disrupt systems. A novel approach has been developed to …

Existing Android malware detection methods are usually hard to simultaneously resist
various obfuscation techniques. Therefore, bytecode-based code obfuscation becomes an …

Abstract Concept drift is a core challenge in classification tasks of data streams. Although
many drift adaptation methods have been presented, most of them assume that labels of all …

Deep learning has been widely used for Android malware family recognition, but current
deep learning-based approaches make the closed-world assumption that malware families …

Context: Recently, many studies have been proposed to address the threat posed by
Android malware. However, the continuous evolution of malware poses challenges to the …

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-
the-art for solving many of the open issues in that field. However, it is very difficult to evaluate …

Several supervised machine learning models have been proposed and used to detect
Android ransomware. These models were trained using different datasets from different …