SCAPEgoat

SCAPEgoat is a modular framework for side-channel analysis (SCA) that simplifies the entire SCA workflow, from trace acquisition to post-processing. It addresses limitations in existing toolkits by offering:

• Efficient file structures for better trace organization.
• Support for standard capture hardware such as ChipWhisperer.
• Optimized memory usage, making it ideal for handling large datasets.
• Modular design, allowing users to utilize individual components or the full framework.

The name SCAPEgoat originates from SCA with high Performance Evaluation and is inspired by WPI’s goat mascot.

📄 Paper: ePrint 2025/499
📖 Documentation: SCAPEgoat Docs

Features

SCAPEgoat provides a robust and modular approach to side-channel analysis with the following key features:

• 🔹 Modular Framework – Use individual components or the entire suite based on your needs.
• 🚀 Optimized Memory Usage – Designed to efficiently handle large datasets, reducing RAM overhead.
• 📊 Comprehensive SCA Metrics – Compute key SCA metrics, including:
  • Signal-to-Noise Ratio (SNR)
  • Test Vector Leakage Assessment (TVLA)
  • Correlation Power Analysis (CPA)
• 📂 Partitioned Data Analysis – Perform trace analysis based on specific indices or index ranges.
• 🔗 Seamless Hardware Integration – Supports ChipWhisperer and other standard SCA tools for trace collection.

Installation

Follow these steps to set up SCAPEgoat:

1. Install ChipWhisperer (Recommended for Smooth Experience)

• While ChipWhisperer is optional if you're only performing post-processing, installing it is recommended for a seamless experience.
• Follow the official ChipWhisperer installation guide.

2. Clone the SCAPEgoat Library

• Clone the SCAPEgoat repository to your system:

  git clone https://github.com/vernamlab/SCAPEgoat.git

• This contains all the necessary modules for trace handling and post-processing.

3. Clone and Set Up cwtvla for Leakage Detection Metrics

• SCAPEgoat relies on cwtvla for certain statistical leakage tests. Clone the chipwhisperer-tvla repository:

  git clone https://github.com/newaetech/chipwhisperer-tvla.git

• Move the cwtvla folder into the WPI_SCA_LIBRARY folder inside the SCAPEgoat library.

4. Ensure Proper Import Paths

Add the SCAPEgoat library to your system path to ensure smooth usability:

import sys
sys.path.append("/path/to/SCAPEgoat")

This allows scripts and notebooks to access SCAPEgoat modules without manual path modifications.

Usage

SCAPEgoat allows users to manage experiments efficiently and perform post-processing to compute various metrics.

1. Initialize Project:

   • Begin by setting up the parent directory for your project. This will be the main directory where all experiments and associated datasets are stored. For each experiment, create a dedicated subdirectory to help organize your files effectively.

2. Dataset Management:

   • For each experiment, datasets can be added in two ways:
     • Using existing data: You can upload previously captured datasets directly into the experiment.
     • Capturing new data: If you need new data, you can use supported devices for capturing traces. Supported devices include:
       • CW devices such as scopes and target boards
       • LeCroy scopes via the pyVISA interface
     • SCAPEgoat allows you to manage data for each experiment flexibly, whether it’s from previously collected data or fresh traces.

3. Post-Processing:

   • Once datasets are available, you can proceed to post-processing, where you compute the desired metrics based on the experiment's needs. SCAPEgoat supports various metrics such as Signal-to-Noise Ratio (SNR), Test Vector Leakage Assessment (TVLA), and Correlation Power Analysis (CPA), among others.

For actual code examples and further guidance, please refer to the demo folder in the SCAPEgoat repository. It contains sample code that demonstrates how to set up experiments, manage datasets, and perform post-processing.

You can also explore datasets from the following sources for example, usage:

• EM for Good: A dataset for the EM traces collected from unmasked AES implementation on CW-lite. You can find it in the EM for Good GitHub repository.
• Bake it Till You Make It: A dataset where a binary neural network is put under stress by increasing temperature. You can find it in the Bake it Till You Make It GitHub repository.

References

1. SCAPEgoat:

   • Mehta, D., Ganji, F. (2025). SCAPEgoat: A Modular Framework for Side-Channel Analysis with Enhanced Memory Efficiency. Available at: https://eprint.iacr.org/2025/499.pdf.

2. Bake it Till You Make It:

   • Mehta, D., Ganji, F., et al. (2025). "Bake it Till You Make It: Heat-Induced Power Leakage from Masked Neural Networks." In TCHES. Available at: https://ojs.ub.ruhr-uni-bochum.de/index.php/TCHES/article/view/11803.

3. EM for Good:

   • Mehta, D., Ganji, F., et al. (2025). "EM for Good: A Dataset for Electromagnetic Trace Collection of Unmasked AES Implementations." Available at: https://github.com/vernamlab/EM-for-good.

License

This project is licensed under the Creative Commons Attribution 4.0 International License.

Contact

If you have any questions or want to report a bug, please email me at dmmehta2@wpi.edu.