Opcodes as predictor for malware
Daniel Bilar · International Journal of Electronic Security and Digital Forensics · 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.