electricsheepafrica/africa-health-cyber-standards

African Healthcare Cybersecurity Standards Dataset

Dataset Description

Synthetic dataset assessing healthcare cybersecurity standards compliance and gaps across African healthcare facilities, paired with secure baseline records. Standards adoption (ISO 27001, NIST CSF, COBIT, national frameworks) in African healthcare is minimal, and the relationship between standards compliance and security outcomes is poorly understood.

Research Gap Addressed

Cybersecurity standards research evaluates compliance in regulated industries with mature governance. African healthcare facilities typically have no standards certification, no internal audit capability, and rely on donor-driven assessments with no follow-up. The relationship between standards gaps and actual incident rates in this context is unstudied. No datasets bridge this gap.

African Healthcare Context

African standards landscape: only South Africa, Nigeria, Kenya, and Ghana have mature national cybersecurity frameworks; most countries have no healthcare-specific cyber standards; ISO 27001 certification is rare outside South African private hospitals; donor-funded facilities may implement standards during project period with no sustainability; and NIST CSF is unknown outside international organisation partnerships. Compliance is paper-based with no technical verification.

Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

Source	URL
ISO 27001 Healthcare Information Security Guidelines	https://www.iso.org/standard/27001
NIST Cybersecurity Framework 2.0	https://www.nist.gov/cybersecurity-framework
African Union Cybersecurity Agenda 2063	https://au.int/en/treaties/african-union-convention-cyber-security-and-personal-data-protection
HIMSS Analytics EMRAM Security Maturity Model	https://www.himss.org/resources/analytics

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: framework_type, compliance_level, last_audit_date, gaps_identified, gaps_remediated, incidents_12mo, maturity_score, certification_status

Engineered Features (computed by features.py):

Feature	Description
compliance_maturity_score	Composite of framework adoption, certification, and audit regularity
incident_severity_correlation	Relationship between compliance gaps and incident frequency/severity
framework_strength	Relative strength of implemented framework based on type and coverage
remediation_capacity	Ability to close gaps based on budget and timeline

Usage

Load from Hugging Face

from datasets import load_dataset

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

Pandas

import pandas as pd

df = pd.read_csv("data/processed/health-cyber-standards_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{health_cyber_standards,
  author = {Electric Sheep Africa},
  title = {African Healthcare Cybersecurity Standards Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/africa-health-cyber-standards},
  type = {Synthetic Dataset}
}

License

CC BY 4.0

Contact

Electric Sheep Africa