SC2026: Screaming Channel 2026 Dataset

SC2026 is a large-scale public dataset for Far-Field Electromagnetic Side-Channel Attacks (FEM-SCAs), also known as Screaming Channel Attacks.
The dataset is designed to support systematic, realistic, and reproducible evaluation of long-range EM side-channel attacks under diverse conditions.

It contains far-field EM traces captured from Bluetooth-enabled IoT devices executing multiple cryptographic algorithms, across different distances and physical barriers.

📌 This dataset accompanies the paper:

A Systematic Far Field EM Side-Channel Evaluation and Introduction of SC2026 Dataset
Not yet published

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🔍 Key Features

• 📡 Far-field EM leakage (wireless, over-the-air)
• 🔐 Multiple cryptographic algorithms
  • AES
  • SM4
  • CRYSTALS-Kyber
• 📏 Multiple attack distances
  • 0 m (coaxial cable baseline)
  • 3 m / 6 m / 9 m / 12 m / 15 m
  • 30 m (open-field scenario)
• 🧱 Physical barriers
  • Plastic
  • Wood
  • Metal
• 🧠 Supports both classical and deep-learning SCAs
  • CPA, Template Attack
  • CNN / MLP / Transformer / Domain Adaptation models
• 📂 NumPy format (.npy) for efficient loading and training

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📦 Dataset Structure

To balance accessibility and research utility, this dataset is provided in two formats:

👁️ Preview (dataset_preview.parquet): A small sample (first 100 traces) containing plaintexts, keys, and trace snippets. Use the "Viewer" tab above to explore the data schema interactively.

⬇️ Full Dataset (.npy): The complete raw traces and metadata are stored in the data/ and metadata/ directories as NumPy files. Researchers should download these files for training and analysis.

The SC2026 dataset is divided into two main subsets:

1️⃣ Profiling Set (High-Quality Reference)

Used for building leakage models.

• Capture method: Coaxial cable
• Purpose: Profiling / training
• Traces per algorithm: 40,000
• Each trace:
  • Averaged over 100 repeated measurements
  • Plus a corresponding single-trace (non-averaged) version
• Algorithms & labels:
  • AES / SM4: plaintext & key
  • Kyber: message bit

📌 This design enables:

• Profiling attacks
• Transfer learning
• Domain adaptation
• Denoising and robustness studies

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2️⃣ Testing Set (Realistic Attack Scenarios)

Used for evaluating attack performance under realistic conditions.

• Capture method: Over-the-air (far-field EM)
• Traces per scenario: 5,000
• No averaging, no repetition
• Covers:
  • Different distances
  • Different environments
  • Different physical barriers

Each scenario is stored as an independent .npy file for clarity and reproducibility.

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📁 Example Directory Layout

SC2026/
├── profiling/
│   ├── AES/
│   │   ├── traces_avg.npy
│   │   ├── traces_single.npy
│   │   ├── plaintext.npy
│   │   └── key.npy
│   ├── SM4/
│   └── Kyber/
│
├── testing/
│   ├── distance_3m/
│   │   ├── AES.npy
│   │   ├── SM4.npy
│   │   └── Kyber.npy
│   ├── distance_15m/
│   ├── distance_30m/
│   ├── barrier_plastic/
│   ├── barrier_wood/
│   └── barrier_metal/
│
└── README.md

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How to Download

Full dataset:

from huggingface_hub import snapshot_download
snapshot_download(repo_id="SCA-HNUST/SC2026", repo_type="dataset", local_dir="<download_path>")

One sub-dataset of choice:

from huggingface_hub import snapshot_download
snapshot_download(repo_id="SCA-HNUST/SC2026",repo_type="dataset",local_dir="<download_path>",allow_patterns="<sub_dataset>/*")

Replace <sub_dataset> with 'AES','SM4','CRYSTALS-Kyber'.

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🧪 Experimental Setup Overview

• Target device: Nordic nRF52 DK (nRF52832)
• Radio: Bluetooth Low Energy (2.4 GHz)

Receiver:

• Ettus N210 USRP
• SBX RF daughterboard
• 24 dBi directional antenna

Signal acquisition:

• Sampling rate: 5 MHz
• Center frequency: 2.272 GHz

Environments:

• Indoor: office corridor
• Outdoor: open field

Both indoor (office corridor) and outdoor (open field) environments are included.

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📜 Citation

If you use SC2026 in your research, please cite:

@article{wang2025sc2026,
  title={A Systematic Far Field EM Side-Channel Evaluation and Introduction of SC2026 Dataset},
  author={Wang, Huanyu and Wang, Xiaoxia and Ge, Kaiqiang and Yao, Jinjie and Tan, Xinyan and Wang, Junnian},
  journal={-----------------------------------},
  year={2025}
}

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🤝 Contact

For questions, feedback, or collaboration, please contact:

Huanyu Wang

School of Computer Science and Engineering
Hunan University of Science and Technology