---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: African Wearable Device Security Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- cybersecurity
- healthcare
- africa
- synthetic
- wearable
task_categories:
- tabular-classification
- other
task_ids:
- tabular-multi-class-classification
---
# African Wearable Device Security Dataset

## Dataset Description

Synthetic dataset of security incidents involving wearable medical devices in African healthcare contexts, paired with secure baseline records. Wearable health devices (fitness trackers, smartwatches, continuous glucose monitors) are proliferating in Africa but security research has not kept pace with adoption.

### Research Gap Addressed

Wearable device security research focuses on FDA-regulated devices in developed markets. African contexts involve low-cost unregulated wearables, smartphone-dependent data sync over mobile networks, and limited user security awareness. No datasets address this emerging threat landscape.

### African Healthcare Context

African wearable adoption relies on low-cost Chinese imports with minimal security, Bluetooth pairing without authentication, and cloud sync to overseas servers with unclear data governance. Many users share devices or accounts. Power constraints limit encryption on-device. Health data often syncs over unsecured public WiFi.

### Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

| Source | URL |
|--------|-----|
| FDA Cybersecurity Guidance for Medical Devices | https://www.fda.gov/medical-devices/digital-health-center-excellence/cybersecurity-medical-devices |
| HIMSS Connected Health Report 2023 | https://www.himss.org/resources/connected-health |
| WHO Digital Health Device Guidelines | https://www.who.int/publications/i/item/9789240070813 |
| INTERPOL IoT Threat Assessment 2024 | https://www.interpol.int/en/Publications/World-Cyberthreat-Assessment |

### Dataset Summary

- **Size:** 10,000 rows (5,000 attack incidents + 5,000 secure baseline records)
- **Features:** 50+ columns including facility metadata, attack characteristics, security posture indicators, impact metrics, and response outcomes
- **Target:** `label` (1 = security incident, 0 = secure baseline)
- **Countries:** 20 African nations with weighted distribution reflecting digital economy and healthcare incident prevalence
- **Intelligence Traceability:** Every attack record includes `intelligence_source` and `intelligence_source_url` fields
- **Synthetic Flag:** All records include `is_synthetic` = 1 for transparency
- **Seed:** 42 for full reproducibility

### Supported Tasks

- **Tabular Classification:** Predict security incidents vs. secure baselines
- **Anomaly Detection:** Identify outlier patterns in healthcare security posture
- **Risk Scoring:** Train models to compute composite security risk scores
- **Feature Engineering:** Domain-specific composite scores included (see Engineered Features below)
- **Benchmarking:** Cross-dataset comparison of African healthcare cybersecurity risks

## Data Acquisition

All records are synthetically generated using weighted-choice parameterisation calibrated against verified real-world intelligence reports. The generation process:

1. **Intelligence Source Mapping:** Each attack type and pattern is derived from documented incidents in the source reports
2. **African Contextualisation:** Country weights, facility types, and infrastructure characteristics reflect African healthcare realities
3. **Balanced Sampling:** Equal representation of attack and benign classes (5,000 each)
4. **Leakage Prevention:** Build pipeline filters out duplicate or near-duplicate records across attack and benign sets
5. **Feature Extraction:** Domain-specific composite scores are computed post-generation

### Key Columns

**Identification & Source:**
- `record_id` — Unique identifier
- `intelligence_source` — Organisation that reported the real-world incident this record is parameterised from
- `intelligence_source_url` — URL to the source report
- `country` — African nation
- `is_synthetic` — Always 1 (transparency flag)

**Core Incident Features:**
device_type, connectivity, firmware_version, bluetooth_enabled, data_encryption, pairing_security, patient_data_exposed, location_tracking

**Engineered Features** (computed by `features.py`):

| Feature | Description |
|---------|-------------|
| `device_security_score` | Composite of encryption, pairing, and connectivity security |
| `firmware_risk` | Risk based on firmware age and update status |
| `data_sovereignty_flag` | Whether data leaves African jurisdiction |
| `exposure_probability` | Probability of data exposure based on device configuration |

## Usage

### Load from Hugging Face

```python
from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/africa-wearable-device-security", split="train")
print(dataset.features)
print(dataset["label"][:10])  # 1 = incident, 0 = baseline
```

### Pandas

```python
import pandas as pd

df = pd.read_csv("data/processed/wearable-device-security_africa_dataset.csv")
X = df.drop(["label", "record_id", "intelligence_source", "intelligence_source_url"], axis=1, errors="ignore")
y = df["label"]
```

### Quick Baseline Classifier

```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

numeric_df = df.select_dtypes(include=["number"]).drop(["label", "is_synthetic"], axis=1, errors="ignore")
X_train, X_test, y_train, y_test = train_test_split(numeric_df, df["label"], test_size=0.2, random_state=42, stratify=df["label"])

clf = RandomForestClassifier(n_estimators=200, random_state=42, n_jobs=-1)
clf.fit(X_train, y_train)
print(f"Accuracy: {clf.score(X_test, y_test):.3f}")
```

## Data Splits

Single `train` split provided. Users should perform stratified train/validation/test splits:

```python
from sklearn.model_selection import StratifiedKFold
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
```

## Limitations

- **Synthetic Data:** All records are synthetically generated and do not represent real individual patients or incidents
- **Simplified Dynamics:** Real-world attack chains are more complex than the feature representation
- **Static Snapshot:** Does not capture temporal evolution of threats or seasonal patterns
- **Generalisation:** Models trained on this data should be validated on real-world African healthcare security data before deployment
- **Ethical Considerations:** While synthetic, the dataset simulates sensitive healthcare security scenarios and should be used responsibly

## Citation

```bibtex
@dataset{wearable_device_security,
  author = {Electric Sheep Africa},
  title = {African Wearable Device Security Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/africa-wearable-device-security},
  type = {Synthetic Dataset}
}
```

## License

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

## Contact

Electric Sheep Africa