  Recent work has shown that adversarial Windows malware samples - referred to
as adversarial EXEmples in this paper - can bypass machine learning-based
detection relying on static code analysis by perturbing relatively few input
bytes. To preserve malicious functionality, previous attacks either add bytes
to existing non-functional areas of the file, potentially limiting their
effectiveness, or require running computationally-demanding validation steps to
discard malware variants that do not correctly execute in sandbox environments.
In this work, we overcome these limitations by developing a unifying framework
that does not only encompass and generalize previous attacks against
machine-learning models, but also includes three novel attacks based on
practical, functionality-preserving manipulations to the Windows Portable
Executable (PE) file format. These attacks, named Full DOS, Extend and Shift,
inject the adversarial payload by respectively manipulating the DOS header,
extending it, and shifting the content of the first section. Our experimental
results show that these attacks outperform existing ones in both white-box and
black-box scenarios, achieving a better trade-off in terms of evasion rate and
size of the injected payload, while also enabling evasion of models that have
been shown to be robust to previous attacks. To facilitate reproducibility of
our findings, we open source our framework and all the corresponding attack
implementations as part of the secml-malware Python library. We conclude this
work by discussing the limitations of current machine learning-based malware
detectors, along with potential mitigation strategies based on embedding domain
knowledge coming from subject-matter experts directly into the learning
process.

Cryptography and Security;Machine Learning