|Title:||A Review on Hybrid Analysis Using Machine Learning for Android Malware Detection|
|Keywords:||A Review on Hybrid Analysis Using Machine Learning for Android Malware Detection|
Nowadays Android is the world's most popular mobile operating system.
Its pervasiveness also provokes the enormous growth of Android malware.
Using machine learning methods to detect Android malware, researchers
have focused on static analysis and dynamic analysis for most. But,
different evasion techniques by shrewd malware authors made those
techniques inadequate and ineffective. Therefore, recent researchers
have turned their attention to the discovery of an effective strategy to
combat. Hybrid analysis which is a fusion of static analysis and
dynamic analysis would be a good candidate for that as it prevails over
the individual shortcomings of static and dynamic analysis with the cost
of complexity. Hybrid analysis has many opportunities as well as
challenges. This research is intended to offer a detailed and systematic
review of hybrid analysis using machine learning techniques for malware
detection in Android. It encompasses leading hybrid analysis research:
their contributions, strengths, and weaknesses. This work also discusses
the challenges, opportunities, and future directions of hybrid analysis
in detecting Android malware.