The full text of this article


Opcodes as predictor for malware
by Daniel Bilar
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 1, No. 2, 2007


Abstract: This paper discusses a detection mechanism for malicious code through statistical analysis of opcode distributions. A total of 67 malware executables were sampled statically disassembled and their statistical opcode frequency distribution compared with the aggregate statistics of 20 non-malicious samples. We find that malware opcode distributions differ statistically significantly from non-malicious software. Furthermore, rare opcodes seem to be a stronger predictor, explaining 12–63% of frequency variation.

Online publication date: Sat, 26-Jan-2008


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