@inproceedings{553c8cd2d98e45e8b75e22e2f2b52754,
title = "Optimization to the Rescue: Evading Binary Code Stylometry with Adversarial Use of Code Optimizations",
abstract = "Recent work suggests that it may be possible to determine the author of a binary program simply by analyzing stylistic features preserved within it. As this poses a threat to the privacy of programmers who wish to distribute their work anonymously, we consider steps that can be taken to mislead such analysis. We begin by exploring the effect of compiler optimizations on the features used for stylistic analysis. Building on these findings, we propose a gray-box attack on a state-of-the-art classifier using compiler optimizations. Finally, we discuss our results, as well as implications for the field of binary stylometry.",
keywords = "Adversarial machine learning, Bayesian optimization, Privacy, Stylometry",
author = "Ben Jacobsen and Sazzadur Rahaman and Saumya Debray",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 2021 Workshop on Research on Offensive and Defensive Techniques in the Context of Man At The End (MATE) Attacks, CheckMate 2021 ; Conference date: 19-11-2021",
year = "2021",
month = nov,
day = "19",
doi = "10.1145/3465413.3488574",
language = "English (US)",
series = "CheckMate 2021 - Proceedings of the 2021 Research on Offensive and Defensive Techniques in the Context of Man At The End (MATE) Attacks, co-located with CCS 2021",
publisher = "Association for Computing Machinery, Inc",
pages = "1--10",
booktitle = "CheckMate 2021 - Proceedings of the 2021 Research on Offensive and Defensive Techniques in the Context of Man At The End (MATE) Attacks, co-located with CCS 2021",
}