Table of Contents

Packing Box Tweet

Experimental toolkit for static detection of executable packing.

Read The Docs Black Hat Arsenal Europe 2022 License: GPL v3

This Docker container is a CLI environment featuring a toolkit that gathers executable analyzers, packing detectors, packers and unpackers but also many tools for generating and manipulating datasets of packed and not-packed executables of different formats (including PE, ELF and Mach-O) for the sake of evaluating static detection techniques and tools, visualizing executables’ layout and automating machine learning pipelines with the support of many algorithms.

Here is what you can see when you start up the Docker container.

The various items integrated in the Packing-Box are defined in the very declarative and easy-to-use YAML format through different configuration files. This makes shaping the scope for evaluations and machine learning model training straightforward and practical for researchers.

Quick Start

Building the image:

# docker build -t dhondta/packing-box .
<<<wait for a while>>>

Starting it up with the current working directory mounted as /mnt/share in the container:

# docker run -it -h packing-box -v `pwd`:/mnt/share dhondta/packing-box

┌──[user@packing-box]──[/mnt/share]────────           ────[]──[12:34:56]──[0.12]────


Items Usage

Items are configured through the YAML configuration files. They consist in:

From within the Packing-Box, the packing-box tool allows to setup and test items.

Operation Description Command
setup Setup an item from its YAML install definition # packing-box setup detector die
test Test an item using a built-in set of test samples # packing-box test packer upx

Afterwards, items are available from the console.

$ die --help
$ upx --help

Mass Packing & Detection

Packers and detectors have their respective dedicated tools for mass operations, packer and detector. They work either on a single file, a complete folder or a special dataset instance (as of the abstraction defined in the pbox package).

$ packer upx path/to/executables --prefix "upx_"

For the detector tool, not selecting any detector will use those selected in detectors.yml as being part of the “superdetector”. Moreover, the --binary option will consider whether the target executable is packed or not and not is precise packer.

$ detector path/to/single-executable -d die -d pypackerdetect
$ detector path/to/executables
<<snipped ; will use "superdetection">>
$ detector path/to/executables -d bintropy --binary
<<snipped ; in this case, as Bintropy only supports binary classification, --binary is necessary>>

Learning Pipeline

Machine Learning models are fine-tuned through the YAML configuration files. They consist in:

  • algorithms.yml: the algorithms that are used with their static or dynamic parameters while training models
  • features.yml: the characteristics to be considered while training and using models

The PREPARE phase, especially feature engineering, is fine-tuned with the features YAML definition. Note that feature extraction is achieved with the pbox package of the Packing-Box while feature derivation and transformation is fine-tuned via the features YAML file.

The TRAIN phase is fine-tuned through the algorithms YAML file by setting the static and/or cross-validation parameters.

Dataset Manipulations

The PREPARE phase, especially dataset generation, is achieved with the dataset tool.

Operation Description Command
make Make a new dataset, either fully packed or mixed with not-packed samples # dataset make dataset -c PE -n 200 -s /path/to/pe
merge Merge two datasets # dataset merge dataset dataset2
select Select a subset of a dataset to create a new one # dataset select dataset dataset2 -q "format == 'PE32'"
update Update a dataset with new samples given their labels # dataset update dataset -l labels.json -s folder-of-executables

Data Visualization

The VISUALIZE phase can be performed with the dataset and visualizer tools.

In order to visualize feature values:

$ dataset plot test-mix byte_0_after_ep byte_1_after_ep --multiclass

In order to visualize samples (aims to compare the not-packed and some packed versions):

$ visualizer plot "PsExec.exe$" dataset -s -l not-packed -l MEW -l RLPack -l UPX

This will work for instance for a structure formatted as such:

  +-- not-packed/PsExec.exe
  +-- packed
        +-- MEW/mew_PsExec.exe
        +-- RLPack/rlpack_PsExec.exe
        +-- UPX/upx_PsExec.exe

Model Manipulations

The TRAIN and PREDICT phases of the pipeline are achieved with the model tool.

Operation Description Command
compare Compare the performance metrics of multiple models # model compare model --dataset dataset --model model2
test Test a model on a given dataset # model test model --name dataset
train Train a model given an algorithm and input dataset # model train dataset --algorithm dt

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