… permission and API calls which an app demand at the time of … using machine learning
techniques, our framework can be easily differentiated between benign or malware-infected apps…

… Moreover this paper uses machine learning because non machine learning approaches
are non-reliable and not efficient. In existing approaches only 30 permissions out of 300 …

… In this subsection, we discuss about the analysis and its types which are used for Android …
from Android. In this study, we perform dynamic analysis of Android apps to build a malware …

… software variants (MSV) in Android Apps that use the Gated Recurrent Unit [GRU] [ANN…
deep learning methods is provided in order to identify the most effective model to identify Android …

… This work investigates Android botnets using static analysis to … The features are then used
to develop effective machine learning … The features are extracted from 1928 Android botnet …

… Android malware detection. Moreover, this paper defines and further illustrates the four classes
of android … proposing a static analysis-based malware detection method with this recent …

… of the selected dataset, the analysis type used, the employed ML techniques, and the chosen
… Android malware detection techniques and provide a solid baseline to machine learning …

… unsupervised machine learning techniques is … Android ransomware. To the best of our
knowledge, performing unsupervised machine learning techniques for the detection of Android …

… extract the permissions from an app. Based on this cognition … method based on TF-IDF and
Machine Learning is proposed. The system permissions are extracted in Android application …

… paper uses Reverse Engineered Android applications’ features and Machine Learning
algorithms … current datasets of malware samples than conventional methods. Secondly, we have …