.
├── attacks  #WF attacks                     
    ├── kfingerprinting: kFP using Random Forest 
    ├── df: Deep Fingerprinting        
    ├── cumul : CUMUL using SVM
    ├── knn: kNN using k Nearest Neighbor  
    ├── decision: Split decision using Random Forest (Used for evaluating Glue) 
    ├── xgboost: Split finding using xgboost (Used for evaluating Glue) 
    ├── split: Cut l-traces according to result from split finding (Used for evaluating Glue) 
    ├── after-split-attack: customized kNN codes for evaluating Glue
    └── random_attack.py: analyze the result from split decision + finding + WF attack 
├── defenses  #WF defenses 
    ├── wtfpad: WTF-PAD defense
    ├── front: FRONT defense
    ├── glue: Glue defense
    └── results: a folder to generate datasets defended by one of the defenses      
├── utils    #some useful tools
    ├── overhead.py: calculate the mean data overhead of front or/and glue (glue noise use +-888 as direction; front noise +-999)
    ├── norm.py: generate a normalized dataset, turning +-888, +-999 to +-1. This is for further evaluation using WF attacks. The rule is that directions are +-1.
    └── rmnoise.py: get clean dataset from noisy dataset. (rm +-999, +-888 packets)         
└── README.md

To run defenses, go to a defense folder. To run attacks, go to an attack folder.

FRONT takes in a dataset folder, output a defended dataset into "defenses/results/" folder

python3 main.py ../../data/tor/

This generates a dataset into results/ folder using FRONT defense.

python3 mp-main.py ../results/glued_trace/ -format ".merge"

This adds front noise to l-traces defended by Glue

python3 main.py ../../data/tor2-5-1/ -n 4000 -b 1 -m 2 -noise True -mode fix

n: number of l-traces; m: l; b: base rate; noise: add noise or not; mode: fix -> all traces are m length; random -> length is randomly chosen from (2, m)

Generate 4000 noisy 2-traces with base rate 1

Go to an attack folder

To evaluate FRONT, First extract features

python3 extract.py ../../defense/results/xxx/

Then

python3 new_main.py(or main.py) ./results/test.npy 

This will generate results of a 10 cross validation result.

To evaluate Glue, Use mp-extract.py to extract features, it will generate features for the first page and the other pages seperately (since they need to be evaluated using two WF models). Then

python3 evaluate.py -m a-saved-model.pkl -o leaf.npy(needed for kFP)/training_data.npy(needed for cumul) -p ./results/test.npy

random-evaluate.py is used under split with decision scenario. used together with random_attack.py.

./run_attack.sh data_folder log_dir

cd after-split-attack, mp-kNN contains customized kNN for split finding case; randomkNN2 contains customized kNN for split decision + finding case

For example, cd mp-kNN, run

./run_attack_head.sh train_folder test_folder log_dir

This evaluate the first split webpages.

./run_attack_other.sh train_folder test_folder log_dir

This evaluate the other split webpages.

Go to "attacks/decision"

python3 run_attack.py -train trainset -test testset -num l

This corresponds to split decision process.
It will generate a ".npy" file telling the prediction of l of all l-traces in testset.
"-num" indicates this testset contains traces of length l.
trainset contains l-traces of different l; testset only contains traces of the same l.

Go to "attack/xgboost"

python3 run_attack.py -train trainset -test testset -mode decision/finding -kdir ../decision/results/testset.npy

This corresponds to split finding process.
It will generate a splitresult.txt file telling where the splits are in a trace.
"-mode" whether you run split decision or not.
Note if mode is decision, then you should give the predicition of "l" using "-kdir "

For most of the codes, they use Python3 as default. Except for kNN codes.

This is only for research purpose. The codes may not be written in a good way. Sorry for that.

Some of the codes are based on the following works. We thank respective authors for being kind to share their code:
[1] Wang et al., "Effective Attacks and Provable Defenses for Website Fingerprinting": https://www.cse.ust.hk/~taow/wf/
[2] Juarez et al., "Toward an Efficient Website Fingerprinting Defense": https://github.com/wtfpad/wtfpad
[3] Hayes and Danezis, "k-fingerprinting: a Robust Scalable Website Fingerprinting Technique": https://github.com/jhayes14/k-FP
[4] Payap Sirinam et al., "Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning": https://github.com/deep-fingerprinting/df