From: LSGAN-AT: enhancing malware detector robustness against adversarial examples
Ref | Defense mechanism | Data type | Data level | Model | Robustnes analysis | Components analysis | AME generation | AME evaluation |
---|---|---|---|---|---|---|---|---|
Li and Li 2020) | Proactive | Drebin, Androzoo | SC, Byte | Ensemble Methods (DL-based) | ● | ○ | ● | ● |
Grosse et al. 2016) | Proactive | Drebin | SC | FFNN | ● | ● | ● | ○ |
Chen et al. 2017) | Reactive | Windows | SC | SecDefender (ML-based) | ○ | ● | ○ | ○ |
Grosse et al. 2017) | Proactive | Drebin | SC | DNN | ○ | ○ | ● | ● |
Khoda et al. 2019) | Proactive | Drebin | SC | Ensemble Methods (ML-based) | ● | ○ | ● | ○ |
Sewak et al. 2020) | Proactive | MALICIA (Nappa et al. 2015) | Opcode | Deep reinforcement learning | ○ | ○ | ● | ○ |
Wang, et al. 2017) | Reactive | Window Audit Log (Berlin et al. 2015), MINST, CIFAR-10 | SC, Pixel (image) | DNN | ● | ○ | ● | ● |
Ours | Proactive | VirusShare, Androzoo | SC | LSGAN | ● | ● | ● | ● |