electricsheepafrica/africa-telemedicine-security

African Telemedicine Security Dataset

Dataset Description

Synthetic dataset of security incidents affecting telemedicine platforms across African healthcare systems, paired with secure baseline records. The COVID-19 pandemic accelerated telemedicine adoption across Africa, but security frameworks have not kept pace with deployment, creating significant vulnerabilities in remote care delivery.

Research Gap Addressed

Telemedicine security research predominantly focuses on developed markets with robust broadband infrastructure and established regulatory frameworks. African telemedicine platforms often operate over mobile networks, use consumer-grade video conferencing tools, and lack dedicated security teams. No comprehensive datasets exist for this unique threat landscape.

African Healthcare Context

African telemedicine relies heavily on mobile connectivity (3G/4G), operates across borders with varying data protection laws, and frequently uses WhatsApp or Zoom for consultations. Platforms often lack end-to-end encryption, session recording for audit, and proper patient identity verification. Power outages can interrupt sessions mid-consultation, creating data integrity risks.

Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

Source	URL
WHO Digital Health Guidelines 2023	https://www.who.int/publications/i/item/9789240070813
HIMSS Telehealth Security Framework	https://www.himss.org/resources/telehealth-security
Africa CDC Digital Health Strategy 2023-2030	https://africacdc.org/digital-health
INTERPOL Cyberthreat Assessment 2025	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: platform_type, authentication_method, encryption_level, session_duration, patient_data_exposed, video_breach, prescription_tampered, recording_stored

Engineered Features (computed by features.py):

Feature	Description
session_security_score	Composite of authentication strength, encryption, and verification status
data_exposure_risk	Probability of data exposure based on platform and security features
compliance_score	Adherence to data residency, privacy, and verification requirements
platform_risk_flag	High-risk platform types with weak default security

Usage

Load from Hugging Face

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/africa-telemedicine-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/telemedicine-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{telemedicine_security,
  author = {Electric Sheep Africa},
  title = {African Telemedicine Security Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/africa-telemedicine-security},
  type = {Synthetic Dataset}
}

License

CC BY 4.0

Contact

Electric Sheep Africa