Towards detecting evasive malware in the age of smart adversaries

Authors
Park, Daniel
ORCID
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Other Contributors
Yener, Bülent, 1959-
Milanova, Ana
Zaki, Mohammed J., 1971-
Pendleton, Marcus
Issue Date
2021-05
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
This dissertation investigates the application of machine learning algorithms in malware detection at various points in the pipeline. We find that although machine learning can be used increase automation and future threat detection, it also faces a trade-off due to its vulnerability against adversarial examples. We demonstrate both the usefulness and vulnerability of machine learning models used in malware detection and take steps towards ensuring a safe adoption of machine learning in the cybersecurity field.
Description
May 2021
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.