Learning framework for program property prediction.
This is a backend for tools such as JSNice (http://jsnice.org/) for JavaScript that can predict program properties such as variable names and types. This backend is designed to extend the tool to multiple programming languages. For this reason, the machine learning machinery is extracted in this tool.
To get a complete tool, one must include a parses for each programming language of interest and train on a lot of code.
We have included an example frontend for JavaScript deminification at http://github.com/eth-srl/UnuglifyJS . This tool work together with the Nice2Server
To compile, first install dependencies (on Ubuntu 14.04):
sudo apt-get install libgoogle-glog-dev libgflags-dev libjsoncpp-dev libmicrohttpd-dev libcurl4-openssl-dev libargtable2-dev cmake
Install Google SparseHash 2.0.2: https://code.google.com/p/sparsehash/downloads/list
And then install json-rpc-cpp from: https://github.com/cinemast/libjson-rpc-cpp
[Optional] Install Google Performance Tools:
- libunwind: http://download.savannah.gnu.org/releases/libunwind/libunwind-1.1.tar.gz
- gperftools: https://code.google.com/p/gperftools/
[Optional] Using Eclipse CDT with C++11: http://stackoverflow.com/a/20101407
Finally, call
./build.sh
Compiling creates a training binary in
bin/training/train
To get options for training, use:
bin/training/train --help
By default, train gets input programs (converted to JSON for example with UnuglifyJS) from the file testdata in the current directory. As a result, it creates files with the trained model.
To predict properties for new programs, start a server after a model was trained:
bin/server/nice2server --logtostderr
Then, the server will predict properties for programs given in JsonRPC format. One can debug and observe deobfuscation from the viewer available in the viewer/viewer.html .