trace
listlengths 400
400
| plaintext
stringlengths 32
32
| ciphertext
stringlengths 32
32
| key (10th round)
stringclasses 1
value | label
stringclasses 1
value |
|---|---|---|---|---|
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691E16801E6949568D642972118EEB4F
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F300CD806CF7453841EFC19BCF34EA51
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36D024461D84B8375FC0F9C04CBAB6BB
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AES_3m_Avg100
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2265723913CCBBF977C17BFF7AEF4060
|
DC39756EAC71C2698A660ABBE5A6159E
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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AEDFB33CC60748AE715B56E277F29100
|
16FD613A0558DBB7062CA87CDEDD97C1
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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EE658500E5D4C4830319AB041CBA83A8
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39FFDA643B787C82D23F23FC2DDC3DAE
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36D024461D84B8375FC0F9C04CBAB6BB
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AES_3m_Avg100
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04F5998004744F041293EDACB6D6087A
|
835E2E2BF777F37BBC67C96CBF959A4B
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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DF5DF1CAC3A280FBFAC4233C272F237E
|
705205917DA8948CE303E464350C0C24
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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20F4E1B4B1CD34B535E8C92560167856
|
3CF3EB827194ADBB5F57C9BB8888E3FB
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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2B323F0434057009272C3165E4C4A8F4
|
21499684DBCF17C51D5BAD8F662C85EE
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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7CAC2B2BA330857B82B843C8EB65307A
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28F341CEFBC13AD8F183905245B8E9D2
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36D024461D84B8375FC0F9C04CBAB6BB
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AES_3m_Avg100
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B4414FD345354306EFF63459BD33A8D7
|
ACBA07E36E42C982404A9F6FD2024F25
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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44F7F1ABBCEF855C2B7410D0F3D1F3CC
|
B882443DCE4CE2583098F7E53C8D9882
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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908C07D48EB3D6B523FDEA7AEDB33873
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4D5052A3E01A785BE626D810C31BE7BE
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36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
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22FF3805DB4352EA28485AF9D4831418
|
FB2D92A9B83746D9F22B1E6406E95FA9
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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3AE8BA14A0E6DB4BC5ED0D3E6C889144
|
0BDE9098A755E504C98D0BD20A9C3762
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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F8D28BE49DAAC4190B1EB2EA8848E092
|
2FA9BA72BBE61E2E433967FAF3168562
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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] |
869B44E5EC59B784603689678BC0101A
|
28FD131558E31D5110CE12740A4DEA3E
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
[
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AC0E0A26AAFCFDE4A6E682370CEA9556
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917E5B9B6B77F36BD051BC911901E8A9
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36D024461D84B8375FC0F9C04CBAB6BB
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AES_3m_Avg100
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BDE6100FBAAEA276E94DFB201C4F5E61
|
05A89387309D65AC3B23F940977C5B40
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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C8E69BB77C9053ED6654F8C1CCF1F953
|
88524D76A769F8EB2A8705DEE0B5C5FC
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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E2D67619E619FB763766CE32CF2B3578
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2B2668CDD76CFA207F5BC2956FFAE209
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36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
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33D515B30D5DBAA55FBF6343D916BFBE
|
106E8F0ED1C206167A844F467E059B27
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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C81280DEBBA6FDD6B742154D128C28A5
|
1F89AA700E6639228E9FD8C8FD3ABB95
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
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4B5531D2FC4F85A173EF617CFA6FDFFF
|
406CC8D92A89D735F31BB4C532C3E691
|
36D024461D84B8375FC0F9C04CBAB6BB
|
AES_3m_Avg100
|
End of preview. Expand
in Data Studio
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
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
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