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HomeArticlesFINGERPRINTING MALICIOUS CODE THROUGH STATISTICAL OPCODE ANALYSIS

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FINGERPRINTING MALICIOUS CODE THROUGH STATISTICAL OPCODE ANALYSIS

by Daniel Bilar

AUTHORS' DESCRIPTION

This paper discusses a detection mechanism for malicious code through statistical analysis of opcode distributions. 67 malware executables were sampled and their opcode frequency distribution statistically compared with the aggregate statistics of twenty non-malicious samples. It was found 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.


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HomeArticlesFINGERPRINTING MALICIOUS CODE THROUGH STATISTICAL OPCODE ANALYSIS

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Installing computer monitoring tools on computers you do not own or do not have permission to monitor may violate local, state or federal law.

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