secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
This tool addresses the need for automated penetration testing in malware detection, which is crucial for security practitioners, but it is incremental as it builds on existing attack methods.
The authors tackled the vulnerability of Windows malware detectors to adversarial attacks by developing secml-malware, a Python library that implements state-of-the-art white-box and black-box attacks to automate adversarial robustness evaluation.
Machine learning has been increasingly used as a first line of defense for Windows malware detection. Recent work has however shown that learning-based malware detectors can be evaded by carefully-perturbed input malware samples, referred to as adversarial EXEmples, thus demanding for tools that can ease and automate the adversarial robustness evaluation of such detectors. To this end, we present secml-malware, the first Python library for computing adversarial attacks on Windows malware detectors. secml-malware implements state-of-the-art white-box and black-box attacks on Windows malware classifiers, by leveraging a set of feasible manipulations that can be applied to Windows programs while preserving their functionality. The library can be used to perform the penetration testing and assessment of the adversarial robustness of Windows malware detectors, and it can be easily extended to include novel attack strategies. Our library is available at https://github.com/pralab/secml_malware.