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cybersec-soc-rgu/R_DNN_IoT

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README.md

README.md

 
 

fully_connected_nn.py

fully_connected_nn.py

 
 

minibatch_optimize_philips.py

minibatch_optimize_philips.py

 
 

minibatch_optimize_provision.py

minibatch_optimize_provision.py

 
 

minibatch_optimize_pt737.py

minibatch_optimize_pt737.py

 
 

minibatch_optimize_snh.py

minibatch_optimize_snh.py

 
 

opt_base_fed_philips_avg.py

opt_base_fed_philips_avg.py

 
 

pt737_adab.py

pt737_adab.py

 
 

pt737_adab_lf.py

pt737_adab_lf.py

 
 

pt737_gbdt.py

pt737_gbdt.py

 
 

pt737_gbdt_lf.py

pt737_gbdt_lf.py

 
 

pt737_tenlite.ipynb

pt737_tenlite.ipynb

 
 

pt737_tensrlte.py

pt737_tensrlte.py

 
 

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The code in this project is used to optimized DNN for IoT security monitoring.

performance comparison with state-of-the-art in terms of robustness and resources savings are presented. A comparsion with TensorFlow Lite model is included in pt737_tensrlte.py script.

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Robust Effective and Resource Efficient Deep Neural networks

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