Malware variants identification based on byte frequency

Malware variants refer to all the new malwares manually or automatically produced from any existing malware. However, such simple approach to produce malwares can change signatures of the original malware to confuse and bypass most of popular signature-based anti-malware tools. In this paper we propose a novel byte frequency based detecting model (BFBDM) to deal with the malware variants identification issue. The primary experimental results show that our model is efficient and effective for the identification of malware variants, especially for the manual variant.