# gchers/wfes

A framework for evaluating Website Fingerprinting attacks/defences, accompaining the paper "Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses" (Cherubin, 2017)
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# Website Fingerprinting Evaluation Suite.

This is a suite for evaluating Website Fingerprinting (WF) attacks and defenses.

It provides:

• a standard interface to use the code from previous attacks/defences,
• a method to estimate security bounds and $(\varepsilon, \Phi)$-privacy of a WF defense.

Code from other researchers (acknowledged below) was adapted to fit the API. With this regard, I tried making as few changes as possible, so as to keep the results close to the original ones; the changes I made are documented by diff files.

An introduction for computing security bounds is at https://giocher.com/pages/bayes.html.

## Installation

mkvirtualenv bayes
pip install -r requirements.txt


Install weka >=3.8 in ~/$WEKA_VERSION/ Then: java -classpath ~/$WEKA_VERSION/weka.jar weka.core.WekaPackageManager -install-package LibSVM


Build attack code by Wang et al.

cd code/attacks/wang && make && cd -


## Reproducing Experiments

This section should allow you to reproduce the experiments, and should help you replicating them with your dataset. I will consider here the dataset by Wang et al. 2014 ("WCN+").

### Getting the WCN+ dataset (and data format explanation)

mkdir -p data/WCN+
cd data/WCN+
wget https://www.cse.ust.hk/~taow/wf/data/knndata.zip
unzip knndata.zip
mv batch original

The dataset has the following format. It is a folder containing files $w-$l, with $w being the web page id,$l indicating the page load.

Each of these files contains, per row:

t_i<tab>s_i


with t_i and s_i indicating respectively time and size of the i-th packet. The sign of s_i indicates the packet's direction (positive means outgoing).

### Defending the dataset

Scripts to defend traces can be called as:

python defend.py --traces $DATASET --out$DEFENDED_DATASET

For example:

python code/defences/WTF-PAD/wtf_pad.py --traces data/WCN+ --out data/WCN+-wtf-pad

To defend all:

DATASET=data/WCN+/original
DST=data/WCN+/

for f in code/defences/*
do
defence=$(basename$f)
echo $f/defend.py --traces$DATASET --out $DST/$defence
done | parallel

NOTE: most of these scripts assume traces' files are in the format $w-$l, with w=0..99, l = 0..89 as in the WCN+ dataset. For decoy-pages, the dataset will need to contain "open world" traces $w, i=0..8999. I didn't have the time to change this in Wang's code. ### Extracting features In order to perform an attack or to compute security bounds you need to first extract feature vectors ("objects") from traces. Each page load$w-$l corresponds to a feature vector, and each feature vector is contained in a file$w-$l.features. In general, you can extract features for attack$attack as follows:

python code/extract_features.py --traces $DATASET --out$FEAT_DIR --attack $attack For a list of attacks do: python code/extract_features.py -h To extract all feature sets for defended traces in data/$defence for all defences, do:

cd code/scripts
bash all_features.sh

#### NOTES

k-NN features. If the argument "--type knn" is added, weights are applied to features. This needs to be done for evaluating the attack. As for computing bounds, this option clearly gives a small advantage (i.e., bounds are smaller); in the paper, however, I computed bounds without this option in order to show that the method is robust w.r.t. small modifications of the feature set.

k-FP features. If the argument "--type kfp" is added, features are extracted using Random Forest as in the paper by Hayes and Danezis. To my understanding, this is an advantage only in the Open World scenario. In experments, I did not use this option for attacks nor bounds, as I observed it produced worse results.

### Classification (attack)

To evaluate an attack, launch:

python code/classify.py --features $FEAT_DIR --train 0.8 --test 0.2 --attack$ATTACK --out $OUT_FNAME The output is a json file. ### Computing bounds Computing bounds is done in two phases, which can be run concurrently. #### Computing distances First, you need to compute the pairwise distances between feature vectors: python code/compute_distances.py --features$FEAT_DIR --out $OUT FYI, the$OUT file can be opened using dill, should you want to inspect it.

An alternative to computing distances (and bounds) on feature vectors is to directly compute them directly on packet sequences (see experiment in Section 7.4):

python code/compute_distances --features $TRACES_DIR --sequences --out$OUT

Note that this did not produce good results (Fig.5) that is, it seems that bounds should be computed on feature vectors rather than directly on packet sequences.

#### Computing bounds

Then, you can compute the bounds using:

python code/bounds.py --distances $DISTANCES --train 0.8 --test 0.2 --out$OUT

The output is a json file, which can be read pretty quickly with Python or with a text editor.