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A Deep Learning-assisted Template Attack Against Dynamic Frequency Scaling Countermeasures

To run an experiment, follow the Jupyter Notebook DLaTA.ipynb.

Organization

The repository is organized as follows:

  • /CNN: Contains modules and configuration files for the Convolutional Neural Network use for classify the operating frequency.
  • /templateAttack: Contains functions and classes for profiling a Template Attack and attacking side-channel traces.
  • /utils: Contains functions for segmenting and interpolating side-channel traces.
  • DLaTA: Jupyter Notebook for running a demo.
.
├── CNN
│   ├── configs/
│   ├── datasets
│   │   └── freq_class_dataset.py
│   ├── models
│   │   ├── custom_layers.py
│   │   ├── resnet.py
│   │   └── resnet_time_series_classifier.py
│   ├── modules
│   │   ├── freq_class_datamodule.py
│   │   └── freq_class_module.py
│   ├── prepare_dataset.py
│   ├── train.py
│   └── utils
│       ├── data.py
│       ├── logging.py
│       ├── module.py
│       ├── trainer.py
│       └── utils.py
├── templateAttack
│   ├── aesSca.py
│   ├── metrics.py
│   ├── preprocess.py
│   ├── TemplateAttack.py
│   └── utils.py
├── utils
│    ├── rescaler.py
│    └── segmentation.py
└── DLaTA.ipynb

Dataset

The DFS_DESYNCH dataset is avaible here on 🤗 Hugging Face.

The dataset is stored in the HDF5 file format (dfs_desynch.h5) and has the following structure:

  • Profiling and Attack groups: The traces are separated into two main groups: "profiling" and "attack". Each group contains 128,000 traces for a total of 256,000 traces.
  • Three Datasets per group: Each group ("profiling" and "attack") consists of three internal datasets:
    • traces: This dataset includes 128,000 power traces, each containing 200,000 time samples. The traces capture the entire AES encryption process preceded by a sequence of random instructions. The traces are pre-processed with a high-pass filter with a 125 kHz cut-off frequency.
    • labels: This dataset provides labels for each power trace in the "traces" dataset, indicating the frequency changes that occurred during the measurement. Each label has two fields:
      • sample: This field denotes the time sample at which a frequency change happens, with values ranging from 0 to 200,000.
      • frequency: This field specifies the new operating frequency starting from the corresponding sample. It can take values from the set {35, 40, 45, 50, 55, 60}.
    • metadata: This dataset contains metadata for each trace, including two members:
      • key: The secret key used for AES encryption.
      • plaintext: The plaintext used for the AES encryption.

How to Download

  1. Download DFS_DESYNCH dataset from 🤗 Hugging Face.

    from datasets import load_dataset

dataset = load_dataset("hardware-fab/DFS_DESYNCH")

  2. Assemble dataset chunks in one (virtual) dataset file.

    python assemble.py --dataset_dir <download_path>

    Replace <download_path> with the actual download path. The assemble.py script is available along with the dataset.

Note

This work is part of [1] available online.

This repository is protected by copyright and licensed under the Apache-2.0 license file.

© 2024 hardware-fab

[1] D. Galli, F. Lattari, M. Matteucci and D. Zoni, "A Deep Learning-assisted Template Attack Against Dynamic Frequency Scaling Countermeasures," in IEEE Transactions on Computers, doi: 10.1109/TC.2024.3477997.