An Explainability-Guided Testing Framework for Robustness of Malware Detectors (original) (raw)

Published August 20, 2023 | Version v2

Software Open

Authors/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

INSTALL.txt

Files (88.6 kB)