  Several solutions ensuring the dynamic detection of malicious activities on
Android ecosystem have been proposed. These are represented by generic rules
and models that identify any purported malicious behavior. However, the
approaches adopted are far from being effective in detecting malware (listed or
not) and whose form and behavior are likely to be different depending on the
execution environment or the design of the malware itself (polymorphic for
example). An additional difficulty is added when these approaches are unable to
capture, analyze, and classify all the execution paths incorporated in the
analyzed application earlier. This suggests that the functionality of the
analyzed application can constitute a potential risk but never explored or
revealed. We have studied some malware detection techniques based on behavioral
analysis of applications. The description, characteristics, and results
obtained from each technique are presented in this article wherein we have also
highlighted some open problems, challenges as well as the different possible
future directions of research concerning behavioral analysis of malware.
