secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
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| Publicado en: | arXiv.org (Dec 13, 2024), p. n/a |
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| Autor Principal: | |
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| Publicado: |
Cornell University Library, arXiv.org
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| Acceso en liña: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2519157086 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2519157086 | ||
| 045 | 0 | |b d20241213 | |
| 100 | 1 | |a Demetrio, Luca | |
| 245 | 1 | |a secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 13, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Detectors | ||
| 653 | |a Python | ||
| 653 | |a Libraries | ||
| 653 | |a Malware | ||
| 653 | |a Machine learning | ||
| 653 | |a Robustness | ||
| 653 | |a Evaluation | ||
| 653 | |a Sensors | ||
| 653 | |a Classifiers | ||
| 700 | 1 | |a Biggio, Battista | |
| 773 | 0 | |t arXiv.org |g (Dec 13, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2519157086/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2104.12848 |