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

As mobile computing becomes an integral part of the modern user experience, malicious
applications have infiltrated open marketplaces for mobile platforms. Malware apps stealthily …

Android users are constantly threatened by an increasing number of malicious applications
(apps), generically called malware. Malware constitutes a serious threat to user privacy …

The widespread adoption of Android devices and their capability to access significant
private and confidential information have resulted in these devices being targeted by …

Abstract Many existing Machine Learning (ML) based Android malware detection
approaches use a variety of features such as security-sensitive APIs, system calls, control …

With Android being the most widespread mobile platform, protecting it against malicious
applications is essential. Android users typically install applications from large remote …

The number of malicious Android apps is increasing rapidly. Android malware can damage
or alter other files or settings, install additional applications, and so on. To determine such …

The drastic increase of Android malware has led to a strong interest in developing methods
to automate the malware analysis process. Existing automated Android malware detection …

The alarming growth rate of malicious apps has become a serious issue that sets back the
prosperous mobile ecosystem. A recent report indicates that a new malicious app for …

With more than two million applications, Android marketplaces require automatic and
scalable methods to efficiently vet apps for the absence of malicious threats. Recent …