Malware for Android is becoming increasingly dangerous to the safety of mobile devices and
the data they hold. Although machine learning (ML) techniques have been shown to be …

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

The first Android-ready “G1” phone debuted in late October 2008. Since then, the growth of
Android malware has been explosive, analogous to the rise in the popularity of Android. The …

Android operating system (OS) has been recently featured as the most commonly used and
ingratiated OS for smartphone ecosystems. This is due to its high interoperability as an open …

In today's rapidly evolving digital landscape, the surge in smartphone usage is paralleled by
an increasing wave of cyberthreats, highlighting the limitations of existing signature-based …

Android malware recognition is the procedure of mitigating and identifying malicious
software (malware) planned to target Android operating systems (OS) that are extremely …

Smart Phones have become an integral part of our lives, offering a wide range of
functionalities and services. However, as the popularity of Android has grown, so has the …

Malware is one of the most significant cybersecurity threats during the era of digital
transformation. An essential tool being used for malware detection is machine learning. A …

In recent times, an upsurge of highly sophisticated and intricate malware has emerged,
becoming one of the most insidious and perilous attack techniques targeting critical …

Information | Free Full-Text | Correction: AlJarrah et al. A Context-Aware Android Malware
Detection Approach Using Machine Learning. Information 2022, 13, 563 Next Article in Journal An …