This repository contains the code for FlowPrint by the authors of the NDSS FlowPrint [1] paper [PDF].
Please cite FlowPrint when using it in academic publications.
This master branch provides FlowPrint as an out of the box tool.
For the original experiments from the paper, please checkout the NDSS branch.
FlowPrint introduces a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic. We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints. These fingerprints can later be reused to recognize known apps or to detect previously unseen apps. The main contribution of this work is to create network fingerprints without prior knowledge of the apps running in the network.
The easiest way to install FlowPrint is using pip
pip install flowprint
If you would like to install FlowPrint manually, please make sure you have installed the required dependencies.
This code is written in Python3 and depends on the following libraries:
- Cryptography
- Matplotlib
- NetworkX
- Numpy
- Pyshark or tshark (works with both backends, tshark is much faster)
- Scikit-learn
To install these use the following command
pip install -U cryptography matplotlib networkx numpy pyshark scikit-learn
If you'd like to use the tshark backend, please install tshark, on Ubuntu this can be done using
sudo apt install tshark
Note that FlowPrint will try to use tshark, if it cannot be found, it will default back to pyshark. It will display a warning message when tshark is not installed.
usage: flowprint.py [-h]
(--detection [FLOAT] | --fingerprint [FILE] | --recognition)
[-b BATCH] [-c CORRELATION], [-s SIMILARITY], [-w WINDOW]
[-p PCAPS...] [-rp READ...] [-wp WRITE]
Flowprint: Semi-Supervised Mobile-App
Fingerprinting on Encrypted Network Traffic
Arguments:
-h, --help show this help message and exit
FlowPrint mode (select up to one):
--fingerprint [FILE] run in raw fingerprint generation mode (default)
outputs to terminal or json FILE
--detection FLOAT run in unseen app detection mode with given
FLOAT threshold
--recognition run in app recognition mode
FlowPrint parameters:
-b, --batch FLOAT batch size in seconds (default=300)
-c, --correlation FLOAT cross-correlation threshold (default=0.1)
-s, --similarity FLOAT similarity threshold (default=0.9)
-w, --window FLOAT window size in seconds (default=30)
Flow data input/output (either --pcaps or --read required):
-p, --pcaps PATHS... path to pcap(ng) files to run through FlowPrint
-r, --read PATHS... read preprocessed data from given files
-o, --write PATH write preprocessed data to given file
-i, --split FLOAT fraction of data to select for testing (default= 0)
-a, --random FLOAT random state to use for split (default=42)
Train/test input (for --detection/--recognition):
-t, --train PATHS... path to json files containing training fingerprints
-e, --test PATHS... path to json files containing testing fingerprints
Run FlowPrint requires three steps:
- Preprocessing: transform
.pcapfiles toflowsthat FlowPrint can interpret.
$ python3 -m flowprint --pcaps <data.pcap> --write <flows.p>
- Fingerprinting: extract
fingerprintsfromflows.
$ python3 -m flowprint --read <flows.p> --fingerprint <fingerprints.json> --split 0.5
- Application: use FlowPrint to recognize apps or detect previously unknown apps.
$ python3 -m flowprint --train <fingerprints.train.json> --test <fingerprints.test.json> --recognition
$ python3 -m flowprint --train <fingerprints.train.json> --test <fingerprints.test.json> --detection 0.1
[1] van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M. & Peter, A. (2020, February). FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic. In 2020 NDSS. The Internet Society.
@inproceedings{vanede2020flowprint,
title={{FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic}},
author={van Ede, Thijs and Bortolameotti, Riccardo and Continella, Andrea and Ren, Jingjing and Dubois, Daniel J. and Lindorfer, Martina and Choffness, David and van Steen, Maarten, and Peter, Andreas}
booktitle={NDSS},
year={2020},
organization={The Internet Society}
}
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