Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

Automated testing of Android apps is essential for app users, app developers, and market
maintainer communities alike. Given the widespread adoption of Android and the …

Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

High penetration of Android applications along with their malicious variants requires efficient
and effective malware detection methods to build mobile platform security. API call …

The unmatched threat of Android malware has tremendously increased the need for
analyzing prominent malware samples. There are remarkable efforts in static and dynamic …

The unrivaled threat of android malware is the root cause of various security problems on
the internet. Although there are remarkable efforts in detection and classification of android …

Malware detection at scale in the Android realm is often carried out using machine learning
techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to …

The Android Application Programming Interface provides the necessary building blocks for
app developers to harness the functionalities of the Android devices, including for interacting …

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this
paper, we attempt to learn the behavior level features of an app from function calls. The …

To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-
malware software, as well as firewalls, require frequent updates and proactive …