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Machine Learning to Deobfuscate Binaries
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README.md

DEBIN portfolio_view

DEBIN is a system that uses machine learning to recover debug information (e.g., names and types) of stripped binaries (x86, x64, ARM). DEBIN is developed at SRI Lab, Department of Computer Science, ETH Zurich as part of the Machine Learning for Programming project.

It is able to distinguish register-allocated and memory-allocated variables with decision-tree-based classification. Moreover, it is capable of predicting meaningful names and types for variables and functions through structured prediction with probabilistic graphical models (with Nice2Predict). These models are learned from thousands of non-stripped binary in open source packages. For mode details, please refer to DEBIN CCS'18 paper and slides.

Setup

Docker

We provide a docker file, which we recommend to start with. To build and run:

$ docker build -t debin .
$ docker run -it debin

Manually

We provide scripts to setup DEBIN manually. The scripts are only tested on Ubuntu 16.04 with gcc 5.4.0):

$ ./install_dependencies.sh  # uses apt-get and requires sudo privileges
$ ./setup.sh

For other platforms, please follow the steps below to setup DEBIN locally:

  1. Install Nice2Predict (according to the instructions in the link)
  2. Install BAP (according to the instructions in the link)
  3. Install python3 dependencies:
$ pip3 install -r requirements.txt
  1. Compile and install the BAP plugin that DEBIN uses:
$ cd ocaml
$ bapbuild -pkg yojson loc.plugin
$ bapbundle install loc.plugin
$ cd ..
  1. Compile the shared library used to produce output:
$ cd cpp
$ g++ -c -fPIC modify_elf.cpp -o modify_elf.o -I./
$ g++ modify_elf.o -shared -o modify_elf.so
$ cd ..

Usage

You can run the following commands to train or test DEBIN, either in docker or locally.

Training

To use DEBIN, one needs to train models with a list of binaries and their debug information. We provide models trained with thousands of binaries for different architectures (x86, x64 and ARM). They can be downloaded through this link or using the following commands:

$ wget https://files.sri.inf.ethz.ch/debin_models.tar.gz
$ tar -zxvf debin_models.tar.gz
$ mv crf/ models/
$ mv variable/ models/
$ rm debin_models.tar.gz

You can also train your own models. Here are the example commands to train the variable classification models and the CRF models using a single sample binary:

$ mkdir -p new_models/variable/x86
$ python3 py/train_variable.py \
          --bin_list examples/bin_list.txt \
          --bin_dir examples/stripped/ \
          --debug_dir examples/debug/ \
          --out_model new_models/variable/x86/ \
          --reg_num_f 100 \
          --off_num_f 100
$ mkdir -p new_models/crf/x86
$ python3 py/train_crf.py \
          --bin_list examples/bin_list.txt \
          --bin_dir examples/stripped/ \
          --debug_dir examples/debug/ \
          --out_model new_models/crf/x86/model \
          --n2p_train Nice2Predict/bazel-bin/n2p/training/train_json \
          --log_dir new_models/crf \
          --valid_labels c_valid_labels

The processes take less than a minute and the trained models are produced in ./new_models. Note that binaires in directory specified by argument --bin_dir should have a symbol table (i.e., .symtab section) so that correct function boundaries are used. To strip all other debug sections except .symtab, one can use command strip -g.

Prediction and Evaluation

First, Nice2Predict server should be run in background:

$ cd Nice2Predict
$ ./bazel-bin/n2p/json_server/json_server \
        --port 8604 \
        --model ../models/crf/x86/model \
        --valid_labels ../c_valid_labels \
        -logtostderr &
$ cd ..

To predict debug information for the example binary lcrack, please use the command below. Please note that for this command, if the input binary already has a symbol table, DEBIN will use the function names and boundaries from that symbol table. Otherwise, function boundaries are inferred by BAP and function names are predicted by DEBIN.

$ python3 py/predict.py \
          --binary examples/stripped/lcrack \
          --output ./lcrack.output \
          --elf_modifier cpp/modify_elf.so \
          -two_pass \
          --fp_model models/variable/x86/ \
          --n2p_url http://localhost:8604
$ readelf -S lcrack.output

The output binary is ./lcrack.output. You can view the section headers of the output and check the predicted debug sections by readelf -S lcrack.output.

To evaluate prediction accuracy, you need the ground truth debug information (sepcified by --debug_info). Note that during evaluation, we assume function boundary is given. Thus, a symbol table should be present for the input binary (the one sepcified by --binary). Ground truth names in the symbol table are not used during prediction and are only used for calculating evalution metrics.

$ python3 py/evaluate.py \
          --binary examples/stripped/lcrack \
          --debug_info examples/debug/lcrack \
          -two_pass \
          --fp_model models/variable/x86/ \
          --n2p_url http://localhost:8604 \
          --stat ./stat.txt
$ cat stat.txt

You can view prediction statistics in ./stat.txt. If you also want an output binary during evaluation, please provide a fully stripped binary (stripped by strip -s and specified by --binary_without_symtab) and use the command below:

$ python3 py/predict_without_func_name.py \
          --binary_with_symtab examples/stripped/lcrack \
          --binary_without_symtab examples/stripped_wo_symtab/lcrack \
          --debug_info examples/debug/lcrack \
          --output ./lcrack.output \
          --elf_modifier cpp/modify_elf.so \
          -two_pass \
          --fp_model models/variable/x86/ \
          --n2p_url http://localhost:8604 \
          --stat ./stat.txt

Citing DEBIN

@inproceedings{He:2018:DPD:3243734.3243866,
 author = {He, Jingxuan and Ivanov, Pesho and Tsankov, Petar and Raychev, Veselin and Vechev, Martin},
 title = {Debin: Predicting Debug Information in Stripped Binaries},
 booktitle = {Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security},
 series = {CCS '18},
 year = {2018},
 isbn = {978-1-4503-5693-0},
 location = {Toronto, Canada},
 pages = {1667--1680},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/3243734.3243866},
 doi = {10.1145/3243734.3243866},
 acmid = {3243866},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {binary code, debug information, machine learning, security},
} 

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