electricsheepafrica/africa-implantable-device-security

African Implantable Device Security Dataset

Dataset Description

Synthetic dataset of security incidents targeting implantable medical devices in African healthcare, paired with secure baseline records. Implantable devices (pacemakers, ICDs, insulin pumps, neurostimulators) contain wireless communication interfaces that create attack surfaces rarely addressed in resource-constrained settings.

Research Gap Addressed

Implantable device security research is dominated by US/EU contexts with FDA-regulated devices and specialised cardiac centres. African contexts involve devices implanted during medical missions with limited follow-up, patients returning to rural areas without monitoring infrastructure, and use of refurbished devices with outdated firmware.

African Healthcare Context

Implantable devices in Africa face unique challenges: limited cardiologist follow-up in rural areas, reliance on device representatives for monitoring visits, patients traveling long distances for battery replacement, use of donated devices nearing end-of-service, and smartphone apps for home monitoring that may not support local networks. Cybersecurity is rarely considered in device procurement.

Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

Source	URL
FDA Medical Device Cybersecurity Guidance	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 Medical Device Safety Guidelines	https://www.who.int/medical-devices
INTERPOL Critical Infrastructure 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, communication_protocol, encryption_enabled, wireless_access, firmware_age_years, vulnerability_count, patient_harm_potential, monitoring_infrastructure

Engineered Features (computed by features.py):

Feature	Description
device_security_posture	Composite of encryption, wireless lockdown, and firmware age
patient_risk_score	Harm potential combined with monitoring gap and vulnerability exposure
maintenance_gap	Risk from delayed follow-up and aging firmware
wireless_attack_surface	Exposure based on protocol security and accessibility

Usage

Load from Hugging Face

from datasets import load_dataset

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

Pandas

import pandas as pd

df = pd.read_csv("data/processed/implantable-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

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:

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

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

License

CC BY 4.0

Contact

Electric Sheep Africa