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In order to prevent detection and evade signature-based scanning methods,which are normally exploited by antivirus software, metamorphic viruses useseveral various obfuscation approaches. They transform their code in newinstances as look entirely or partly different and contain dissimilar sequencesof string, but their behavior and function remain unchanged. This obfuscationprocess allows them to stay away from the string based signature detection. Inthis research, we use a statistical technique to compare the similarity betweentwo files infected by two morphed versions of a given metamorphic virus. Ourproposed solution based on static analysis and it uses the histogram of machineinstructions frequency in various offspring of obfuscated viruses. We useEuclidean histogram distance metric to compare a pair of portable executable(PE) files. The aim of this study is to show that for some particularobfuscation methods, the presented solution can be exploited to detect morphedvarieties of a file. Hence, it can be utilized by non-string based signaturescanning to identify whether a file is a version of a metamorphic virus or not.

Metamorphic Virus Variants Classification Using Opcode Frequency Histogram