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

At present, smartphones running the Android operating system have occupied the leading
market share. However, due to the Android operating system's open-source nature, Android …

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

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

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

Android malware severely threaten system and user security in terms of privilege escalation,
remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and …

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

Is Android malware classification a solved problem? Published F1 scores of up to 0.99
appear to leave very little room for improvement. In this paper, we argue that results are …

In the ever-changing cyber threat landscape, evolving malware threats demand a new
technique for their detection. This paper puts forward a strategy for distinguishing malware …

The evolution of mobile malware poses a serious threat to smartphone security. Today,
sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via …