An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions

dc.contributor.authorDevine, Sean
dc.contributor.authorBastian, Nathaniel
dc.date.accessioned2020-12-24T19:08:44Z
dc.date.available2020-12-24T19:08:44Z
dc.date.issued2021-01-05
dc.description.abstractThe use of machine learning (ML) has become an established practice in the realm of malware classification and other areas within cybersecurity. Characteristic of the contemporary realm of intelligent malware classification is the threat of adversarial ML. Adversaries are looking to target the underlying data and/or models responsible for the functionality of malware classification to map its behavior or corrupt its functionality. The ends of such adversaries are bypassing the cybersecurity measures and increasing malware effectiveness. We develop an adversarial training based ML approach for malware classification under adversarial conditions that leverages a stacking ensemble method, which compares the performance of 10 base ML models when adversarially trained on three data sets of varying data perturbation schemes. This comparison ultimately reveals the best performing model per data set, which includes random forest, bagging and gradient boosting. Experimentation also includes stacking a mixture of ML models in both the first and second levels in the stack. A first level stack across all 10 ML models with a second level support vector machine is top performing. Overall, this work reveals that a malware classifier can be developed to account for potential forms of training data perturbation with minimal effect on performance.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.102
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70714
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAccountability, Evaluation, and Obscurity of AI Algorithms
dc.subjectadversarial training
dc.subjectai system assurance
dc.subjectcybersecurity
dc.subjectmachine learning
dc.subjectmalware detection
dc.titleAn Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions
prism.startingpage827

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
0082.pdf
Size:
288.21 KB
Format:
Adobe Portable Document Format