Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.

Nazim S, Alam MM, Rizvi SS, Mustapha JC, Hussain SS, Suud MM

Open source

DOI
10.1371/journal.pone.0318542
Published
2025
Container
PloS one
Publisher
Not recorded
Open access
yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0318542,
  title = {Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.},
  author = {Nazim S and Alam MM and Rizvi SS and Mustapha JC and Hussain SS and Suud MM},
  year = {2025},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0318542},
  url = {https://doi.org/10.1371/journal.pone.0318542}
}

RIS

TY  - JOUR
TI  - Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.
AU  - Nazim S
AU  - Alam MM
AU  - Rizvi SS
AU  - Mustapha JC
AU  - Hussain SS
AU  - Suud MM
PY  - 2025
JO  - PloS one
DO  - 10.1371/journal.pone.0318542
UR  - https://doi.org/10.1371/journal.pone.0318542
ER  -

APA

S, N., MM, A., SS, R., JC, M., SS, H., & MM, S. (2025). Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.. PloS one. https://doi.org/10.1371/journal.pone.0318542

Source records