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
🔍 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
📦 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
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.
📁 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
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'.
🧪 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.
📜 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}
}
🤝 Contact
For questions, feedback, or collaboration, please contact:
Huanyu Wang
School of Computer Science and Engineering
Hunan University of Science and Technology