/k-FP

Benchmarks for the k-FP WF attack

Primary LanguagePython

k-FP

Benchmarks for the k-FP WF attack

The attack works on trace files containing direction of packets and timing of packets. In the feature extraction process there is the ability to fold in packet size features but this is currently not used.

安装必要库：requirements.txt

数据集下载地址： Hidden Services: https://drive.google.com/open?id=1S5ra--6m1m7ZliyvYsUsTehc6PcJhLjx Alexa: https://drive.google.com/open?id=1xTrJhdqezzdUaFYpMhBPNS10i7AY1ahT Unmonitored: https://drive.google.com/open?id=1qvjnnQumpGh-Tq7oF-5uC3_sBaS3WDB2

Following these steps for k-FP results:

  1. Run python k-FP.py --dictionary --mon_type alexa (for Alexa dataset) or python k-FP.py --dictionary --mon_type hs (for hidden services dataset) to extract and save features for each traffic instance.
  2. For closed world results, run python k-FP.py --RF_closedworld --mon_type alexa or python k-FP.py --RF_closedworld --mon_type hs.
  3. For open world results, first build distances that will be used for classification by running python k-FP.py --distances --mon_type alexa or python k-FP.py --distances --mon_type hs.
  4. For open world classification, run python k-FP.py --distance_stats --mon_type alexa --knn 6 or python k-FP.py --distance_stats --mon_type hs --knn 6, where --knn is the number of neighbours used for final classification.

nohup python k-FP.py --distances --mon_type hs > xxx.log > 2&1 &

Results

Training on 60% of monitored set and 5% of unmonitored set.

Closed world

Alexa dataset - Accuracy 93-95%

HS dataset - Accuracy 83-86%

Open world

Results for knn == 1.

Alexa dataset - TPR = 92-96% FPR = .9-1.6%

HS dataset - TPR = 85-87% FPR = .04-.3%

Disclaimer

The original code and datasets were lost in a drive failure. I have attempted to re-create them as faithfully as possible but there may be some issues. If you find any please report them to me.