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Related Experiment Videos

Real-Time Phishing Campaign Detection for Healthcare Organizations: An Explainable AI Approach Using Semantic

Georgios Feretzakis1, Dimitrios Karapiperis1, Sarandis Mitropoulos1

  • 1School of Science and Technology, Hellenic Open University, Patras, Greece.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

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PhishCluster-Health effectively detects coordinated phishing campaigns targeting healthcare by using specialized medical terms and brand analysis. This advanced system achieves perfect recall, enhancing cybersecurity for sensitive health information.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Healthcare Informatics

Background:

  • Healthcare organizations are increasingly targeted by sophisticated phishing attacks.
  • These threats leverage medical terminology and exploit patient trust to compromise sensitive data.
  • Existing phishing detection methods may not adequately address the nuances of healthcare-specific attacks.

Purpose of the Study:

  • To introduce PhishCluster-Health, a specialized framework for detecting coordinated phishing campaigns targeting the healthcare sector.
  • To enhance phishing detection by integrating domain-specific knowledge and advanced machine learning techniques.
  • To improve the security of healthcare infrastructure against targeted cyber threats.

Main Methods:

  • Developed PhishCluster-Health, a healthcare specialization of the PhishCluster framework.
Keywords:
Phishing detectionexplainable AIhealthcare cybersecurityreal-time threat detectionsemantic embeddingsstreaming clustering

Related Experiment Videos

  • Utilized transformer-based semantic embeddings (all-MiniLM-L6-v2) and density-based streaming clustering.
  • Incorporated a curated medical and insurer brand lexicon, a domain-centric tokenizer, and calibrated distance thresholds.
  • Evaluated performance on a benchmark dataset of 10,011 URLs, including 1,511 malicious healthcare-targeted URLs.

    • Main Results:

    • Achieved 100% campaign-level recall on the in-distribution benchmark.
    • Attained Adjusted Rand Index (ARI) = 1.0 and Normalized Mutual Information (NMI) = 1.0.
    • Demonstrated a mean latency of 9.76 ms per URL.
    • SHAP analysis indicated domain structural patterns contribute 66.8% to feature importance in a complementary classifier.

    Conclusions:

    • PhishCluster-Health demonstrates high efficacy in detecting coordinated healthcare phishing campaigns.
    • The specialized approach significantly improves detection accuracy and efficiency.
    • Further validation is needed, and limitations such as compromised legitimate domains and DGA scenarios are acknowledged.