  In this paper, we argue that machine learning techniques are not ready for
malware detection in the wild. Given the current trend in malware development
and the increase of unconventional malware attacks, we expect that dynamic
malware analysis is the future for antimalware detection and prevention
systems. A comprehensive review of machine learning for malware detection is
presented. Then, we discuss how malware detection in the wild present unique
challenges for the current state-of-the-art machine learning techniques. We
defined three critical problems that limit the success of malware detectors
powered by machine learning in the wild. Next, we discuss possible solutions to
these challenges and present the requirements of next-generation malware
detection. Finally, we outline potential research directions in machine
learning for malware detection.
