  Multi-scanner Antivirus systems provide insightful information on the nature
of a suspect application; however there is often a lack of consensus and
consistency between different Anti-Virus engines. In this article, we analyze
more than 250 thousand malware signatures generated by 61 different Anti-Virus
engines after analyzing 82 thousand different Android malware applications. We
identify 41 different malware classes grouped into three major categories,
namely Adware, Harmful Threats and Unknown or Generic signatures. We further
investigate the relationships between such 41 classes using community detection
algorithms from graph theory to identify similarities between them; and we
finally propose a Structure Equation Model to identify which Anti-Virus engines
are more powerful at detecting each macro-category. As an application, we show
how such models can help in identifying whether Unknown malware applications
are more likely to be of Harmful or Adware type.

Cryptography and Security;Artificial Intelligence;Machine Learning