Challenges in high accuracy of malware detection

Abstract

Malware is a threat to the computer users regardless which operating systems and hardware platforms that they are using. Microsoft Windows is the most popular operating system and the popularity also make it the most favourite platform to be attacked by the adversaries. Current detection for Windows relies on the signature based detection which is fairly fast although suffers undetected binaries. Here, we propose a method to increase the detection rate of malware by manipulating machine learning methods. Our focus is on the Microsoft Windows binaries.