Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
dc.contributor.author | Fleshman, William | |
dc.contributor.author | Raff, Edward | |
dc.contributor.author | Zak, Richard | |
dc.contributor.author | McLean, Mark | |
dc.contributor.author | Nicholas, Charles | |
dc.date.accessioned | 2019-02-06T15:50:41Z | |
dc.date.available | 2019-02-06T15:50:41Z | |
dc.date.issued | 2018-10-18 | |
dc.description | Proceedings of the AAAI Fall 2018 Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurity | en_US |
dc.description.abstract | As 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.uri | http://ceur-ws.org/Vol-2269/FSS-18_paper_11.pdf | en_US |
dc.format.extent | 8 pages | en_US |
dc.genre | conference papers and proceedings | en_US |
dc.identifier | doi:10.13016/m2lze2-cvuq | |
dc.identifier.citation | William 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.pdf | en_US |
dc.identifier.uri | http://hdl.handle.net/11603/12715 | |
dc.language.iso | en_US | en_US |
dc.publisher | AAAI | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.rights | This 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.rights | Copyright © by the paper’s authors. | |
dc.subject | static malware | en_US |
dc.subject | subterfuge | en_US |
dc.subject | machine learning | en_US |
dc.subject | anti-virus | en_US |
dc.title | Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus | en_US |
dc.type | Text | en_US |
Files
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 2.56 KB
- Format:
- Item-specific license agreed upon to submission
- Description: