electricsheepafrica/africa-hospital-iot-security

African Hospital IoT Security Dataset

Dataset Description

Synthetic dataset of security incidents in hospital IoT ecosystems across African healthcare facilities, paired with secure baseline records. Smart hospital devices (connected beds, infusion pumps, patient monitors, HVAC, access control) create vast attack surfaces that are largely unmanaged in African contexts.

Research Gap Addressed

Hospital IoT security research focuses on large Western hospitals with mature network operations centres and device management platforms. African hospitals often lack network segmentation, device inventories, and the ability to distinguish medical from non-medical IoT traffic. Existing datasets do not reflect this unmanaged IoT reality.

African Healthcare Context

African hospital IoT includes: imported smart beds with default credentials, infusion pumps with WiFi but no encryption, patient monitors broadcasting unencrypted data, building automation systems (HVAC, lighting) on same network as EMR, and shadow IoT (staff personal devices, vendor equipment) with no inventory. Network segmentation is rare. IT staff cannot distinguish medical device traffic from other devices.

Intelligence Sources

This dataset is parameterised from verified real-world intelligence:

Source	URL
HIMSS IoT Security Framework	https://www.himss.org/resources/iot-security
NIST IoT Cybersecurity Guidance (SP 800-213)	https://csrc.nist.gov/publications/detail/sp/800-213/final
WHO Healthcare Technology Management Guidelines	https://www.who.int/medical-devices
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: device_type, network_segment, vlan_isolation, default_credentials, firmware_updated, shadow_iot, medical_device_flag, data_exfiltrated

Engineered Features (computed by features.py):

Feature	Description
network_segmentation_score	Quality of device network isolation
device_hygiene_score	Composite of firmware update, credential, and visibility status
iot_attack_surface	Exposure based on network position, credentials, and medical criticality
shadow_iot_risk	Risk specifically from unmanaged devices

Usage

Load from Hugging Face

from datasets import load_dataset

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

License

CC BY 4.0

Contact

Electric Sheep Africa