Update Readme.md

Author: Richardhongyu

Readme.md

@@ -1,26 +1,38 @@
 # Attack code
 
-## The composition of the directoty
-
-- 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 to attack your models
-
-- You need to pass your args to attack_for_traffic.py to attack models. EN:You need to try your own models before you attack it.
-- Example: python model_name.py --model model_name --others
+## What can I do with these files?
+
+1. You can use attack_for_AE/log/traffic.py to attack different datasets with different models in DE.
+2. You can find different models in the DFR/happy/lenet/DFR_log/happy_log/lenet_log.py.
+3. 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?
+
+1. [url](https://pan.baidu.com/s/1z6F8n5GpKqA2yjRtY-Uojw)
+    password:6mw8
+
+2. 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
 
@@ -29,9 +41,7 @@ keras
 
 ## Some important tips
 
-- You can write your own model that you want to attack in keras. And you need to follow the rules in the 3_attack_code/model_name.py. 
-- I write some comments for attacking models in the 3_attack_code/attack_for_traffic.py.
-- I write some comments for model building in the 3_attack_code/happy.py.
-- You can find the specific implements of differential_evolution in the 3_attack_code/differential_evolution.py.
- 
-## GOOD LUCKY TO YOUR TRAVEL IN AI!
+- 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.