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Detection of unknown malware with high accuracy is always a challenging task. Therefore, in this paper we study the classification of unknown malware by two methods. In the first/regular method, similar to other authors (Mehdi et al. Proceedings of the 11th Annual conference on Genetic and evolutionary computation 2009, Moskovitch et al. Intelligence and Security Informatics 2008, Ravi and Manoharan Int J Comput Appl 43(17):12–16 2012) approaches we select the features by taking all dataset in one group and in second method, we select the features by partitioning the dataset in the range of file 5 KB size. We find that the second method detect the malwares with ~8.7 % more accurate than the first/regular method.

Improving the Detection Accuracy of Unknown Malware by Partitioning the Executables in Groups