  Malware detection has become a challenging task due to the increase in the
number of malware families. Universal malware detection algorithms that can
detect all the malware families are needed to make the whole process feasible.
However, the more universal an algorithm is, the higher number of feature
dimensions it needs to work with, and that inevitably causes the emerging
problem of Curse of Dimensionality (CoD). Besides, it is also difficult to make
this solution work due to the real-time behavior of malware analysis. In this
paper, we address this problem and aim to propose a feature selection based
malware detection algorithm using an evolutionary algorithm that is referred to
as Artificial Bee Colony (ABC). The proposed algorithm enables researchers to
decrease the feature dimension and as a result, boost the process of malware
detection. The experimental results reveal that the proposed method outperforms
the state-of-the-art.
