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A Decade of Healthcare Cyber Threats: Empirical Analysis, Evidence-Based Prioritisation, and AI Threat Model

arXiv Security Archived Aug 04, 2026 ✓ Full text saved

arXiv:2608.00901v1 Announce Type: new Abstract: Healthcare systems face persistent and evolving cyber threats, yet how adversarial tactics and techniques have shifted over time has not been systematically characterised using empirical, multi-source data. This paper analyses 1,214 threat records drawn from three authoritative sources: the MITRE ATT&CK behavioural framework, the CISA Known Exploited Vulnerabilities catalogue, and the NIST vulnerability database, covering 44 validated healthcare-ta

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    Computer Science > Cryptography and Security [Submitted on 2 Aug 2026] A Decade of Healthcare Cyber Threats: Empirical Analysis, Evidence-Based Prioritisation, and AI Threat Model Sadia Mehrin Rahi, Ayesha Siddika, Istiyak Hasan Maruf, Adrita Rahman Tory, Muhammad Aminur Rahaman, Khondokar Fida Hasan Healthcare systems face persistent and evolving cyber threats, yet how adversarial tactics and techniques have shifted over time has not been systematically characterised using empirical, multi-source data. This paper analyses 1,214 threat records drawn from three authoritative sources: the MITRE ATT&CK behavioural framework, the CISA Known Exploited Vulnerabilities catalogue, and the NIST vulnerability database, covering 44 validated healthcare-targeting threat entities from 2017 to 2024. We show that attacker behaviour has shifted measurably toward stealth-oriented tactics: defense evasion remained the dominant tactic throughout the observation period, consistently accounting for 15-20% of observed technique use from 2017 to 2024, while persistence declined from 11.2% to zero and initial access from 9.0% to zero over the same period. We further demonstrate that existing detection guidance is structurally misaligned with where attacker effort is concentrated, with the least-covered techniques receiving the most adversarial attention. A convergence analysis links 679 confirmed exploited vulnerabilities to a single dominant behavioural technique, identifying a common addressable chokepoint across the vulnerability and behavioural surfaces. Finally, we identify 42 high-priority techniques representing immediate detection opportunities and show that this set of techniques maps directly to emerging threats against AI-integrated clinical systems. Comments: 15 pages Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.00901 [cs.CR]   (or arXiv:2608.00901v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.00901 Focus to learn more Submission history From: Khondokar Fida Hasan [view email] [v1] Sun, 2 Aug 2026 00:08:32 UTC (865 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 04, 2026
    Archived
    Aug 04, 2026
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