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# 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
```text
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:
```python
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:
```python
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:
```bibtex
@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
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