Machine learning contributions on the field of security and privacy of android

Nowadays, users are storing their data on their smartphones which are connected to the Internet almost perma-nently. Installed apps request several permissions and therefore access to users' personally identifiable information (PII). This may result to a potential leakage of users PII which is a privacy issue. In recent years, machine learning is contributing to the development of various research fields. In this paper, we show to which extent ML has contributed to the field of security and privacy of android ecosystems. For that we conduct a state of the art that includes studies using ML approaches. Our finding shows that machine learning makes a good contribution to privacy and security. Most of the studies use either supervised learning (clas-sification using mainly support vector) or unsupervised learning (using mainly agglomerative clustering). Machine learning (ML) is proving its efficiency in classification for protecting privacy in Android ecosystems.