Publication Date
Fall 2013
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
Metamorphic malware changes its internal structure with each infection, while maintaining its original functionality. Such malware can be difficult to detect using static techniques, since there may be no common signature across infections. In this research we apply a score based on Singular Value Decomposition (SVD) to the problem of metamorphic detection. SVD is a linear algebraic technique which is applicable to a wide range of problems, including facial recognition. Previous research has shown that a similar facial recognition technique yields good results when applied to metamorphic malware detection. We present experimental results and we analyze the effectiveness and efficiency of this SVD-based approach.
Recommended Citation
Jidigam, Ranjith Kumar, "Metamorphic Detection Using Singular Value Decomposition" (2013). Master's Projects. 330.
DOI: https://doi.org/10.31979/etd.838t-v2qr
https://scholarworks.sjsu.edu/etd_projects/330
