In recent years, mobile devices such as smartphones, tablets and wearables have become the new paradigm of user–computer
interaction. The increasing use and adoption of such devices is also leading to an increased number of potential security risks.
The spread of mobile malware, particularly on popular and open platforms such as Android, has become a major concern.
This paper focuses on the bad-intentioned Android apps by addressing the problem of selecting the key features of such
software that support the characterization of such malware. The accurate detection and characterization of this software is
still an open challenge, mainly due to its ever-changing nature and the open distribution channels of Android apps. Maximum
relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied
for feature selection on the well-known Android Malware Genome (Malgenome) dataset, attaining interesting results on the
most informative features for the characterization of representative families of existing Android malware.
