Table 1 Comparison of related existing surveys which are peer-reviewed and focusing adversarial machine learning attacks
From: Machine learning security and privacy: a review of threats and countermeasures
Research paper | Publication year | Survey type | Analysis of all attack types | Analysis criteria/ protocol | Analysis on (domain) | Solutions examined | Limitations identified |
|---|---|---|---|---|---|---|---|
I. Rosenberg et al. [48] | 2021 | Traditional | ✗ | ✗ | Cyber security | ✓ | ✓ |
M. Goldblum et al. [49] | 2020 | Traditional | ✓ | ✗ | Data poisoning | ✓ | ✓ |
M. Rigaki et al. [50] | 2021 | Traditional | ✗ | ✗ | Privacy attacks | ✓ | ✓ |
Z. Wang et al. [51] | 2022 | Traditional | ✗ | ✓ | Poisoning attacks | ✓ | ✗ |
M. Pitropakis et al. [52] | 2019 | Systematic | ✗ | ✓ | Machine learning | ✓ | ✓ |
A. Shafee et al. [53] | 2021 | Traditional | ✗ | ✗ | Privacy attacks | ✓ | ✓ |
P. Bountakas et al. [54] | 2023 | Traditional | ✗ | ✗ | Audio, cyber-security, NLP, computer vision | ✓ | ✓ |
N. Martins et al. [55] | 2020 | Systematic | ✗ | ✓ | Intrusion and malware detection | ✓ | ✓ |
G. R. Machado et al. [56] | 2021 | Traditional | ✗ | ✗ | Image classification | ✓ | ✓ |
A. Alotaibi et al. [57] | 2023 | Traditional | ✗ | ✗ | Intrusion detection system | ✓ | ✗ |
This study | 2023 | Traditional | ✓ | ✓ | AML attack types | ✓ | ✓ |