# 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