electricsheepafrica/africa-legacy-emr-security

African Legacy EMR Security Dataset

Dataset Description

Synthetic dataset of security incidents targeting legacy electronic medical record systems in African healthcare facilities, paired with secure baseline records. Many African facilities continue using end-of-life EMR systems due to budget constraints, creating a critical but under-studied vulnerability vector.

Research Gap Addressed

Legacy system security is well-documented in enterprise contexts but poorly understood in African healthcare where OpenMRS, DHIS2, and donated Western EMR systems operate on unsupported Windows versions with no patching infrastructure. Existing EMR security datasets focus on modern cloud-native systems, not the fragmented reality of African healthcare IT.

African Healthcare Context

African healthcare facilities commonly run Windows XP/7-based EMRs donated from European hospitals, use OpenMRS instances without security updates, or rely on hybrid Excel/paper systems. Dedicated IT staff are rare; patching is irregular due to connectivity issues. Intermittent power causes database corruption, and offline sync conflicts create data integrity issues.

Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

Source	URL
Kaspersky Legacy Systems Healthcare Report 2021	https://securelist.com/attacks-on-healthcare/101468/
HIMSS Healthcare Cybersecurity Survey 2024	https://www.himss.org/what-we-do-insights/cybersecurity-survey
Andersen Nigeria Healthcare Cyber Report 2024	https://andersenlab.com/insights/healthcare-cybersecurity
INTERPOL Africa Cyberthreat Assessment 2025	https://www.interpol.int/en/Publications/World-Cyberthreat-Assessment
WHO Global Strategy on Digital Health 2020-2025	https://www.who.int/publications/i/item/9789240020924

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: emr_system, eol_os, unpatched, no_encryption_transit, no_encryption_rest, weak_passwords, no_audit_logging, no_mfa

Engineered Features (computed by features.py):

Feature	Description
vulnerability_exposure_score	Weighted sum of exposed vulnerabilities
security_posture_score	Composite of positive security controls
impact_severity_score	Weighted sum of data breach and system impact indicators
is_legacy_system	Binary flag for Windows XP/7 or Meditech legacy systems
is_open_source	Binary flag for OpenMRS or DHIS2 systems
high_records_affected	Binary flag for >10,000 records affected
extended_downtime	Binary flag for >24 hours downtime
recovery_likely	Binary for detection + containment + recovery

Usage

Load from Hugging Face

from datasets import load_dataset

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

License

CC BY 4.0

Contact

Electric Sheep Africa