Published August 20, 2023
| Version v1
Software
Open
An Explainability-Guided Testing Framework for Robustness of Malware Detectors
Creators
- 1. CSIRO's Data61
- 2. Hong Kong University of Science and Technology (GZ)
- 3. Shanghai Jiao Tong University
- 4. Peng Cheng Laboratory
Description
This is the artifact accompanying the paper "Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors", accepted by ESEC/FSE 2023.
If you would like to use this project in your research, please cite our paper:
Ruoxi Sun, Minhui Xue, Gareth Tyson, Tian Dong, Shaofeng Li, Shuo Wang, Haojin Zhu, Seyit Camtepe, and Surya
Nepal. Mate! Are you really aware? An explainability-guided testing framework for robustness of malware detectors.
In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of
Software Engineering, 2023.
Files
main.ipynb
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