Malware Identification by Statistical Opcode Analysis
CSEF · 2010 Mathematics & Software Honorable_mention Award
Overview
Objectives/Goals This project determined the efficacy of statistical analysis of program assembly instruction (opcode) frequencies to identify Malware from Goodware. Methods/Materials Malware and Goodware binaries were obtained and a python script was created to extract opcode frequencies from specific parts of these files. Naive Bayes models and Kmeans based models were then trained using these executables. These models were tested using a different set of programs to determine their efficacy at identifying Malware from Goodware. Results The best Naive Bayes model had a recall of 1 for Malware and .8 for Goodware. Conclusions/Discussion Differences in opcode frequencies can differentiate Malware from Goodware. Certain instructions occur much more frequently in one group than in the other; these differences can be used to identify the two types of programs.
Summary statement
This project examines models that differentiate Malware from Goodware using the frequencies of program assembly instructions.
Help received
Communicated with mentor Joshua Kroll ; Pamela Durkee proofread papers and guidance
Awards (1)
- Honorable Mention
Competition history
- CSEF 2010
Resources
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Source: California Science & Engineering Fair public projects