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Var-CNN

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Code for the paper "Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning" (PETS 2019)

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# Var-CNN

This repository contains code for the following paper:

[Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep 
Learning](https://arxiv.org/abs/1802.10215) (PETS 2019,
[link to presentation](https://docs.google.com/presentation/d/1Pry0nfHXvDhWYbHjf2QK75Rquc51s9rxK0g2XSNVeIk/edit?usp=sharing))

[Sanjit Bhat](https://sanjit-bhat.github.io), David Lu, 
[Albert Kwon](http://www.albertkwon.com), and 
[Srini Devadas](https://people.csail.mit.edu/devadas/).

## Dependencies
1. Ensure that you have a functioning machine with an NVIDIA GPU inside it. 
The model will take significantly longer to run on a CPU. 
2. Make sure you have the TensorFlow/Keras deep learning stack installed.
For detailed instructions, see 
[this link](https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415) 
under the "Software Setup" section. For our experiments, we used Ubuntu 
16.04 LTS, CUDA 8.0, CuDNN v6, and TensorFlow 1.3.0 as a backend for Keras 2.0.8.
3. To install all required Python packages, simply issue the following command:
```pip install -r requirements.txt```.

## Control Flow
The first step in running our model is to place the adequate amount
of raw packet sequences in the ```data_dir``` folder. **Each** monitored
website needs to have **at least** ```num_mon_inst_train``` + ```num_mon_inst_test```
instances, and there needs to be **at least** 
```num_unmon_sites_train``` + ```num_unmon_sites_test``` unmonitored sites.

If you use the Wang et al. data format (i.e., each line representing a 
new packet with the relative time and direction separated by a space), 
then we have that supported in ```wang_to_varcnn.py```. Otherwise, you will need 
to modify ```wang_to_varcnn.py```, or you can write your own glue code to move
to the **Wang et al. format**.

After setting up the data and specifying the parameters in ```config.json```,
you can run all parts of our code just by issuing a ```python run_model.py```
command. After that, our programs will be called in the following sequence:

1. ```wang_to_varcnn.py```: This parses the ```data_dir``` folder; extracts direction,
time, metadata, and labels; and stores all the monitored and unmonitored
traces in ```all_closed_world.npz``` and ```all_open_world.npz```,
respectively, in the ```data_dir``` folder.
2. ```preprocess_data.py```: This uses the data in 
```all_closed_world.npz``` to pick a random
```num_mon_inst_train``` and ```num_mon_inst_test``` instances of each
of the ```num_mon_sites``` monitored sites for the training and test sets, respectively.
It also performs a similar random split for the unmonitored sites (using the
```all_open_world.npz``` file) and preprocesses
all of these traces to scale the metadata, change to inter-packet timing, etc.
Finally, it saves the direction data, time data, metadata, and labels
to ```.h5``` files to conserve RAM during the training process.
3. ```run_model.py```: This is the main file that first calls the prior two files.
Next, it loads the model architectures from either ```var_cnn.py``` or 
```df.py```, trains the models, saves their predictions, and
calls ```evaluate.py``` for evaluation. 
4. During training, ```data_generator.py``` generates new batches of data in 
parallel. Since large datasets can contain hundreds of thousands 
of traces, ```data_generator.py``` uses ```.h5``` files to
access the traces for one batch without loading the entire dataset into memory.
5. ```evaluate.py```: This first calculates metrics for each of the
in-training combinations specified in ```mixture```. Then,
it averages each of their predictions together and reports metrics for the
overall out-of-training ensemble. It saves all metrics to the ```job_result.json```
file.

## Parameters
```config.json``` provides the configuration settings to all the 
other programs. We describe its parameters in further detail below:

1. ```data_dir```: This relative path provides the location of the "raw"
packet sequences (e.g., the "0", "1", "0-0", "0-1" files in Wang et al.'s
dataset). Also, it later stores the ```all_closed_world.npz``` and
```all_open_world.npz``` files generated by ```wang_to_varcnn.py``` and the
```.h5``` data files generated by ```preprocess_data.py```.
2. ```predictions_dir```: After training the model, ```run_model.py``` 
generates predictions for the test set and stores them in this directory.
```evaluate.py``` later uses them to calculate test metrics.
3. ```num_mon_sites```: The number of monitored websites.
Each of the ```num_mon_sites``` sites in ```data_dir``` must have at least
```num_mon_inst_train``` + ```num_mon_inst_test``` instances.
4. ```num_mon_inst_train```: The number of monitored instances used for training.
5. ```num_mon_inst_test```: The number of monitored instances used for testing.
6. ```num_unmon_sites_train```: The number of unmonitored sites used for training.
Each site has one instance.
7. ```num_unmon_sites_test```: The number of unmonitored sites used for testing.
Each site has one instance, and these unmonitored websites are different
from those used for training.
8. ```model_name```: The model name. Either "var-cnn" or "df".
9. ```batch_size```: The batch size used during training. For Var-CNN,
we found that a batch size of 50 works well. The recommended batch size
for DF is 128.
10. ```mixture```: The mixture of ensembles used during training and
evaluation. Each of the inner arrays represent models combined in-training.
```run_model``` will save the predictions for every such in-training combination.
Subsequently, ```evaluate_ensemble``` will report metrics for these individual
models as well as the overall out-of-training ensemble (i.e., the average
of the individual predictions). Note: this functionality only works
with Var-CNN (in fact, deep fingerprinting will automatically default
to using ```[["dir"]]```). Also, do not use two in-training combinations 
with the same components as their prediction files will be overwritten.
Default: ```[["dir", "metadata"], ["time", "metadata"]]``` for Var-CNN.
11. ```seq_length```: The length of the input sequence fed into the CNN
(default: 5000).
We use this parameter right from the start when scraping the raw data.
12. ```df_epochs```: The number of epochs used to train DF (default: 30).
13. ```var_cnn_max_epochs```: The maximum number of epochs used to train 
Var-CNN (default: 150). The ```EarlyStopping``` callback often cuts off training 
much sooner -- whenever validation accuracy fails to increase.
14. ```var_cnn_base_patience```: The "patience" (i.e., number of epochs
of no validation accuracy improvement) until we decrease the learning 
rate of Var-CNN and stop training (default: 5). We implement this functionality
in the ```ReduceLROnPlateau``` and ```EarlyStopping``` callbacks
inside ```var_cnn.py```.
15. ```dir_dilations```: Whether to use dilations with the
direction ResNet (default: true).
16. ```time_dilations```: Whether to use dilations with the
time ResNet (default: true).
17. ```inter_time```: Whether to use the inter-packet time
(i.e., time between two packets) or the relative time (i.e.,
time from the first packet) for timing data (default: true, i.e., we do use
inter-packet time).
18. ```scale_metadata```. Whether to scale metadata to zero mean
and unit variance (default: true).

## Citation
If you find Var-CNN useful in your research, please consider citing:

@article{bhat19,
	  title={{Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning}},
	  author={Bhat, Sanjit and Lu, David and Kwon, Albert and Devadas, Srinivas},
	  journal={Proceedings on Privacy Enhancing Technologies},
	  volume={4},
	  pages={292--310},
	  year={2019}
	}

## Contact
sanjit.bhat (at) gmail.com

davidboxboro (at) gmail.com

kwonal (at) mit.edu

devadas (at) mit.edu

Any discussions, suggestions, and questions are welcome!

Var-CNN

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