From: LSGAN-AT: enhancing malware detector robustness against adversarial examples
Ref | Attack phase | Data type | Data level | Model | Dynamic Feature | Model Comparison | Model transferability | AME evaluation |
---|---|---|---|---|---|---|---|---|
Hu and Tan 2017) | Training | Malware by Website (malwr.com) | SC | GAN | ● | ○ | ○ | ● |
Li and Li 2020) | Training | SC, Byte | Ensemble Methods (DL-based) | ○ | ● | ● | ○ | |
Grosse et al. 2016) | Training | Drebin | SC | FFNN | ○ | ● | ● | ○ |
Chen et al. 2017) | Testing | Windows | SC | EvnAttack (ML-based) | ○ | ● | ○ | ○ |
Grosse et al. 2017) | Training | Drebin | SC | DNN | ○ | ● | ○ | ● |
Khoda et al. 2019) | Training | Drebin | SC | Ensemble Methods (ML-based) | ○ | ● | ● | ○ |
Wang et al. 2019b) | Training | Drebin | SC | JSMA and NN | ○ | ● | ● | ● |
Yuan et al. 2020) | Training | VirusTotal (“VirusTotal”. 2021), Kaggle 2015 (“Microsoft Malware Classification Challenge (BIG 2015), Chocolatey (“Chocolatey—The package manager for Windows” 2021) | Byte | GAPGAN | ○ | ● | ● | ● |
Ours | Training | VirusShare (“VirusShare.com”. 2021), Androzoo | SC | LSGAN | ● | ● | ● | ● |