Attack code
What can I do with these files?
- You can use attack_for_AE/log/traffic.py to attack different datasets with different models in DE.
- You can find different models in the DFR/happy/lenet/DFR_log/happy_log/lenet_log.py.
- Meanwhile you can randomly attactk models by random_arrack/random_generate/random_generate_for_log.py.
where can I get the pretrained models and datasets?
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url password:6mw8
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The composition of the directoty in the url:
- 0_AEEA_dataset Our datasets are here including a log dataset,a traffic dataset.
- 1_model_for_traffic This directory includes our pretrained models in traffic dataset. You can get the training process by the tensorboard.
- 2_model_for_log This directory includes our pretrained models in traffic dataset. You can get the training process by the tensorboard.
- 3_attack_code You can get all the training code in this directory. If you want to test your own model, you can add your model in the your_model_name.py and put your pretrained model here. You can also try different ways to attck models, such as random attack,differential evolution. It is convinient to try your models in different dataset.
- 4_EVALUATION
- 5_For_TEST_h5 You can get the accurate attack resutls.
How can I attack my models?
- You need to pass your args to attack_for_traffic.py(or other attack files) to attack models.
EN:You need to train your own models before you attack it.
- Example: python model_name.py --model model_name --other_args
- To get more args, you can read attack_for_traffic.py.
Envirionment
tensorflow_1_13_gpu keras
Some important tips
- You can write your own model that you want to attack in keras. And you need to follow the examples in the happy.py.
- I write some comments for attacking models in the attack_for_traffic.py.
- I write some comments for model building in the happy.py.
- You can find the specific implements of differential_evolution in the differential_evolution.py.