A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild
<|reference_start|>A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild: Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack framework that evaluates the security risks of existing learning-based Windows malware detection systems under the black-box setting. MalGuise first employs a novel semantics-preserving transformation of call-based redividing to concurrently manipulate both nodes and edges of malware's control-flow graph, making it less noticeable. By employing a Monte-Carlo-tree-search-based optimization, MalGuise then searches for an optimized sequence of call-based redividing transformations to apply to the input Windows malware for evasions. Finally, it reconstructs the adversarial malware file based on the optimized transformation sequence while adhering to Windows executable format constraints, thereby maintaining the same semantics as the original. MalGuise is systematically evaluated against three state-of-the-art learning-based Windows malware detection systems under the black-box setting. Evaluation results demonstrate that MalGuise achieves a remarkably high attack success rate, mostly exceeding 95%, with over 91% of the generated adversarial malware files maintaining the same semantics. Furthermore, MalGuise achieves up to a 74.97% attack success rate against five anti-virus products, highlighting potential tangible security concerns to real-world users.<|reference_end|>
@article{ling2024a,
    title={A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for
  Evading Learning-based Windows Malware Detection in the Wild},
    author={Xiang Ling and Zhiyu Wu and Bin Wang and Wei Deng and Jingzheng Wu and
  Shouling Ji and Tianyue Luo and Yanjun Wu},
    journal={arXiv preprint arXiv:2407.02886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02886},
    primaryClass={cs.CR}
}