SoK: An Essential Guide For Using Malware Sandboxes In Security Applications: Challenges, Pitfalls, and Lessons Learned
<|reference_start|>SoK: An Essential Guide For Using Malware Sandboxes In Security Applications: Challenges, Pitfalls, and Lessons Learned: Malware sandboxes provide many benefits for security applications, but they are complex. These complexities can overwhelm new users in different research areas and make it difficult to select, configure, and use sandboxes. Even worse, incorrectly using sandboxes can have a negative impact on security applications. In this paper, we address this knowledge gap by systematizing 84 representative papers for using x86/64 malware sandboxes in the academic literature. We propose a novel framework to simplify sandbox components and organize the literature to derive practical guidelines for using sandboxes. We evaluate the proposed guidelines systematically using three common security applications and demonstrate that the choice of different sandboxes can significantly impact the results. Specifically, our results show that the proposed guidelines improve the sandbox observable activities by at least 1.6x and up to 11.3x. Furthermore, we observe a roughly 25% improvement in accuracy, precision, and recall when using the guidelines to help with a malware family classification task. We conclude by affirming that there is no "silver bullet" sandbox deployment that generalizes, and we recommend that users apply our framework to define a scope for their analysis, a threat model, and derive context about how the sandbox artifacts will influence their intended use case. Finally, it is important that users document their experiment, limitations, and potential solutions for reproducibility<|reference_end|>
@article{alrawi2024sok:,
    title={SoK: An Essential Guide For Using Malware Sandboxes In Security
  Applications: Challenges, Pitfalls, and Lessons Learned},
    author={Omar Alrawi, Miuyin Yong Wong, Athanasios Avgetidis, Kevin Valakuzhy,
  Boladji Vinny Adjibi, Konstantinos Karakatsanis, Mustaque Ahamad, Doug
  Blough, Fabian Monrose, Manos Antonakakis},
    journal={arXiv preprint arXiv:2403.16304},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16304},
    primaryClass={cs.CR}
}