Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus

Simple item page
dc.contributor.authorFleshman, William
dc.contributor.authorRaff, Edward
dc.contributor.authorZak, Richard
dc.contributor.authorMcLean, Mark
dc.contributor.authorNicholas, Charles
dc.date.accessioned2019-02-06T15:50:41Z
dc.date.available2019-02-06T15:50:41Z
dc.date.issued2018-10-18
dc.descriptionProceedings of the AAAI Fall 2018 Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurityen_US
dc.description.abstractAs machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an agreed upon test set to benchmark malware detection systems on pure classification performance. Instead we tackle the problem by creating a new testing methodology, where we evaluate the change in performance on a set of known benign & malicious files as adversarial modifications are performed. The change in performance combined with the evasion techniques then quantifies a system’s robustness against that approach. Through these experiments we are able to show in a quantifiable way how purely ML based systems can be more robust than AV products at detecting malware that attempts evasion through modification, but may be slower to adapt in the face of significantly novel attacks.en_US
dc.description.urihttp://ceur-ws.org/Vol-2269/FSS-18_paper_11.pdfen_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2lze2-cvuq
dc.identifier.citationWilliam Fleshman, Edward Raff, Richard Zak, Mark McLean, Charles Nicholas, Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus, Proceedings of the AAAI Fall 2018 Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurity, 2018, http://ceur-ws.org/Vol-2269/FSS-18_paper_11.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/12715
dc.language.isoen_USen_US
dc.publisherAAAIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsCopyright © by the paper’s authors.
dc.subjectstatic malwareen_US
dc.subjectsubterfugeen_US
dc.subjectmachine learningen_US
dc.subjectanti-virusen_US
dc.titleStatic Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virusen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection