| Title | Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes |
| Publication Type | Conference Proceedings |
| Year of Publication | 2021 |
| Authors | Lucas, Keane, Sharif, Mahmood, Bauer, Lujo, Reiter, Michael K., Shintre, Saurabh |
| Conference Name | ASIA CCS '21: Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security |
| Date Published | 06/2021 |
| Keywords | 2021: July, CMU |
| Abstract | Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization frameworks to mislead such DNNs while preserving the functionality of binaries. Unlike prior attacks, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white- and black-box settings, and found that it often achieved success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can foil over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by attacks, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning. |
| DOI | doi.org/10.1145/3433210.3453086 |
| Citation Key | node-81244 |
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes