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catboost_info
catboost_info
 
 
data
data
 
 
detection
detection
 
 
results
results
 
 
test_efficiency_realtime
test_efficiency_realtime
 
 
.DS_Store
.DS_Store
 
 
Attacks.ipynb
Attacks.ipynb
 
 
Attacks_industrial.ipynb
Attacks_industrial.ipynb
 
 
Attacks_industrial_gb.ipynb
Attacks_industrial_gb.ipynb
 
 
README.md
README.md
 
 
df_results.pkl
df_results.pkl
 
 
df_results_gb.pkl
df_results_gb.pkl
 
 
functions.py
functions.py
 
 
logo_fondos_incibe.png
logo_fondos_incibe.png
 
 
model_utils.py
model_utils.py
 
 
original_results.pkl
original_results.pkl
 
 
original_results_gb.pkl
original_results_gb.pkl
 
 
transfer_results_envm.csv
transfer_results_envm.csv
 
 
transfer_results_evcs.csv
transfer_results_evcs.csv
 
 
transferability_results.pkl
transferability_results.pkl
 
 
transferability_results_gb.pkl
transferability_results_gb.pkl
 
 

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Adversarial evasion attacks and detection in industrial contexts

Fondos_INCIBE This repository is part of the project "CiberIA: Investigación e Innovación para la Integración de Ciberseguridad e Inteligencia Artificial" (Proyecto C079/23), financed by "European Union NextGeneration-EU, the Recovery Plan, Transformation and Resilience", through INCIBE.

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Adversarial evasion attack and detection analysis in industrial contexts.

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  • Jupyter Notebook 65.2%
  • Python 34.8%