Inferring Smartphone Application Types Via Inaudible Charging Sound: A New Acoustic Side-Channel Attack
Authors/Creators
Description
Inferring Smartphone Application Types Via Inaudible Charging Sound: A New Acoustic Side-Channel Attack
IEEE Franklin V. Taylor Memorial Award - Nominees in IEEE SMC 2025, Vienna.
Dataset
The dataset contains 36 audio recordings captured during smartphone charging sessions across different application scenarios.
Recording Specifications
- **Sampling Rate**: 192 kHz (high-fidelity acoustic capture)
- **Recording**: Continuous full-session audio (no pre-sliced segments; slicing done during preprocessing)
- **Format**: WAV (uncompressed)
- **Microphone**: Double X0 pickup microphone
- **Battery Level**: All recordings at 100% capacity
- **Environment**: Controlled ambient conditions for reproducible acoustic feature extraction
Device Specifications
Smartphones:
- **Redmi Note 12T**: Mid-range Android device
- **Huawei Nova 6**: Legacy device for cross-generational analysis
Chargers:
- **Xiaomi 67W Charger**: Fast charging (QC/PD protocols)
- **Ugreen Dual-Port 20W**: Baseline charging (17W single-port mode)
Application Categories
| Category | Applications | Protocol |
| ----------------------- | -------------------------------------------------- | ------------------------------------- |
| **Games** | Minecraft, Sky: Children of the Light, PUBG Mobile | 3D scene navigation and gameplay |
| **Social** | QQ, DingTalk | Text messaging and transmission |
| **Entertainment** | Douyin (TikTok), Kuaishou (Kwai) | Video playback with content switching |
| **Shopping** | JD, Pinduoduo | Product browsing and selection |
File Structure
```
dataset/
├── 01.wav ~ 36.wav # Audio recordings
└── labels.json # Metadata with app names and device info
```
Each entry in `labels.json` contains:
```json
{
"app_name": "Application name",
"device": {
"phone": "Smartphone model",
"charger": "Charger specification"
}
}
```