flowprint 0.0.4

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

FlowPrint

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.

Introduction

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.

Installation

The easiest way to install FlowPrint is using pip

pip install flowprint

Manually

If you would like to install FlowPrint manually, please make sure you have installed the required dependencies.

Dependencies

This code is written in Python3 and depends on the following libraries:

• Cryptography
• Matplotlib
• NetworkX
• Numpy
• Pyshark
• Scikit-learn

To install these use the following command

pip install -U cryptography matplotlib networkx numpy pyshark scikit-learn

Usage

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:

1. Preprocessing: transform .pcap files to flows that FlowPrint can interpret.

$ python3 -m flowprint --pcaps <data.pcap> --write <flows.p>

2. Fingerprinting: extract fingerprints from flows.

$ python3 -m flowprint --read <flows.p> --fingerprint <fingerprints.json> --split 0.5

3. 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

References

[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.

Bibtex

@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}
}