Towards detecting evasive malware in the age of smart adversaries
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Authors
Park, Daniel
Issue Date
2021-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
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
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY