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User action detection toolkit. Spy on mobile phone apps using machine learning-based attacks on the encrypted traffic.
Python
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README.md

Diplomka

This project (so far) tries to replicate the results obtained in the attached paper (paper.pdf).

Usage

First, make sure you have all the dependencies:

$ pip install -r requirements.txt

Additionally, you will need to provide training data in the form of pcap files:

$ ls ~/mypcaps/*.pcap
one.pcap
two.pcap
...

To generate the dataset of features extracted from the pcap files, use dataset.py:

$ ./dataset.py ~/mypcaps/
[1/3] Processing: /home/tbabej/mypcaps/one.pcap
[2/3] Processing: /home/tbabej/mypcaps/two.pcap
[3/3] Processing: /home/tbabej/mypcaps/three.pcap
Writing to mypcaps.csv

To train and evaluate the SVM on this data, use svm.py:

$ ./svm.py mypcaps.csv --optimize
Searching for optimal parameters..
Used parameters: C=2048.0, gamma=0.03125
Success rate: 0.744769874477

The dataset and svm commands have more options, explore their documentation via:

$ ./dataset.py -h
$ ./svm.py -h

Useful

Pre-generated .csv training data sets are available in trainingsets/ directory.

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