This project is an implementation of the paper:
"Traffic Morphing: An efficient defense against statistical traffic analysis"
by Charles Wright, Scott Coull, and Fabian Monrose.

It's written in Python and it uses GLPK to solve Linear Programming
problems.

In the topdir you can find:

* morpheus.py: You pass it a source and a target probability
    distribution of packet lengths and it writes a morphing matrix in
    Matrix Market format to a given output file.
    You can then use those morphing matrices to your heart's content.

* dreams/: Contains 'dream', a small library that understands what a
    morphing matrix is. The dreams/ directory has child directories
    containing 'dream' in different programming languages.
    Dream can answer questions like: "What possible shapes can a
    packet of 420 bytes take for this morphing matrix?" or "Give me a
    target packet length for a packet of 12 bytes right now."

* data/: The data/ directory contains some potentially useful
    packet length probability distributions.

* analysis/: The analysis/ directory contains a script which helps you
    decide whether using traffic morphing, is worth it. That is,
    whether the overhead of traffic morphing is sufficiently smaller
    than the overhead of directly sampling the probability
    distributions.

    Given the packet size probability distributions of your source and
    target protocols and an appropriate morphing matrix, it morphs
    packets using direct sampling _and_ traffic morphing, and plots
    the overhead of both methods. Looking at the resulting plots
    should help you decide if traffic morphing is a good idea.

* ACKNOWLEDGMENTS: A kind and cheerful text file that thanks people
    who helped this project.

* TODO: A text file listing a couple of stuff that should be coded.

* FAQ: A file containing Frequently Asked Questions.

* LICENSE: A file containing this project's software license.

* README: Hmmmm...