Publication Date
Winter 2017
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Thomas Austin
Third Advisor
Fabio di Troia
Keywords
index of coincidence, malware, machine learning
Abstract
In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.
Recommended Citation
Gurnani, Bhavna, "Malware Detection using the Index of Coincidence" (2017). Master's Projects. 507.
DOI: https://doi.org/10.31979/etd.9mkp-kstb
https://scholarworks.sjsu.edu/etd_projects/507
