A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
<|reference_start|>A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection: Machine learning malware detectors are vulnerable to adversarial EXEmples, i.e. carefully-crafted Windows programs tailored to evade detection. Unlike other adversarial problems, attacks in this context must be functionality-preserving, a constraint which is challenging to address. As a consequence heuristic algorithms are typically used, that inject new content, either randomly-picked or harvested from legitimate programs. In this paper, we show how learning malware detectors can be cast within a zeroth-order optimization framework which allows to incorporate functionality-preserving manipulations. This permits the deployment of sound and efficient gradient-free optimization algorithms, which come with theoretical guarantees and allow for minimal hyper-parameters tuning. As a by-product, we propose and study ZEXE, a novel zero-order attack against Windows malware detection. Compared to state-of-the-art techniques, ZEXE provides drastic improvement in the evasion rate, while reducing to less than one third the size of the injected content.<|reference_end|>
@article{rando2024a,
    title={A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples
  in Malware Detection},
    author={Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli},
    journal={arXiv preprint arXiv:2405.14519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14519},
    primaryClass={cs.LG}
}