Getting Started In this section are the steps to reproduce our experiments. 1. Prerequisites You need to install the following packages to run this project: Docker and Docker Compose to run our experiments Python-3 to plot the results in the project's Jupyter Notebook Wget, Tar and Sed to run the initial scripts to configure the repository 2. Setup First, download and unpack the zip file. You will get a folder called yali-main. You should copy the .env.example file and rename it to .env. After that, you need to prepare the environment to run our experiments. Run the following command line: $ ./setup.sh This will download the datasets, build the docker image and create the necessary folders for the project. 3. Running Now, you can run the following command line: $ ./run.sh MODE There are the following values for MODE: all: Run all games, the resources analysis and embedding analysis speedup: Run the speedup analysis with the benchmark game embeddings: Run the embedding analysis resources: Run only the resources analysis malware: Run the experiment to detect classes of malware game0 Run Game 0 game1: Run Game 1 game2: Run Game 2 game3: Run Game 3 discover: Run an experiment that tries to discover the obfuscator Statistics The Statistics folder contains Jupyter Notebooks that plot the data generated by the experiments. Each notebook describes each chart and the steps to develop them. There are the following notebooks: EmbeddingResults: Presents information about the accuracy of the dgcnn and cnn models with different representations GameResults: Presents information about the 4 games proposed in our work ResourceResults: Presents information about resource consumption (memory and time) of each model StrategiesResults: Presents the distance between the histograms of the original programs and the histograms generated by the obfuscators Repository We maintain this project in this git repository.