{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:35:45Z","timestamp":1793381745754,"version":"4.5.0"},"reference-count":36,"publisher":"CERN","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Commission","award":["101070176"],"award-info":[{"award-number":["101070176"]}],"id":[{"id":"https:\/\/ror.org\/00k4n6c32","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Open Res Europe"],"abstract":"<jats:p>The increasing integration of Artificial Intelligence (AI) into Cyber-Physical Systems (CPS) presents complex cybersecurity challenges, necessitating a reevaluation of traditional threat assessment. The KINAITICS project addresses these evolving threats by conducting in-depth research into cyber-kinetic attacks, where malicious cyber activities manifest as real-world physical disruptions. The project is also dedicated to developing resilient, AI-driven defense mechanisms.\n                This paper outlines KINAITICS\u2019 foundational work, including the creation of a tailored KINAITICS Threat Matrix (KTM). This innovative framework systematically identifies, categorizes, and assesses threats unique to AI-integrated CPS. The paper details the KTM\u2019s practical application across five high-stakes use cases, ranging from safeguarding nuclear facility simulations to protecting electronic health record (EHR) systems from sophisticated phishing attacks.\n                A central focus of the KINAITICS project is the rigorous development and evaluation of both offensive and defensive AI tools. These tools are designed to investigate, understand, and mitigate the multifaceted threats posed by cyber-kinetic adversaries. The overarching objective is to significantly enhance the resilience of critical infrastructures against advanced cyber-physical threats, ensuring the continued safety, security, and operational integrity of systems vital to modern society.<\/jats:p>","DOI":"10.12688\/openreseurope.21717.1","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:04:07Z","timestamp":1778825047000},"update-policy":"https:\/\/doi.org\/10.68336\/openreseurope.updatepolicy","source":"Crossref","is-referenced-by-count":1,"title":["From Simulation to Healthcare: KINAITICS' AI Framework for Cyber-Physical Security"],"prefix":"10.68336","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6682-705X","authenticated-orcid":true,"given":"Paolo","family":"Marcheschi","sequence":"first","affiliation":[{"name":"BioInformatica Traslazionale ed eHealth (BITE), Fondazione Toscana Gabriele Monasterio per la Ricerca Medica e di Sanita Pubblica, Pisa, Tuscany, 56011, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Pisani","sequence":"additional","affiliation":[{"name":"BioInformatica Traslazionale ed eHealth (BITE), Fondazione Toscana Gabriele Monasterio per la Ricerca Medica e di Sanita Pubblica, Pisa, Tuscany, 56011, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Dalmiani","sequence":"additional","affiliation":[{"name":"BioInformatica Traslazionale ed eHealth (BITE), Fondazione Toscana Gabriele Monasterio per la Ricerca Medica e di Sanita Pubblica, Pisa, Tuscany, 56011, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13904","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"111875","unstructured":"1. KINAITICS Consortium: KINAITICS: Cyber-kinetic attacks using Artificial Intelligence. 2024. Reference Source (https:\/\/kinaitics.eu)"},{"key":"111876","unstructured":"2. European Commission: Cyber-kinetic attacks using Artificial Intelligence. CORDIS EU Research Results 2022. Reference Source (https:\/\/cordis.europa.eu\/project\/id\/101070176)"},{"key":"111877","unstructured":"3. ENISA: ENISA Threat Landscape 2025. European Union Agency for Cybersecurity;Oct. 2025. Reference Source (https:\/\/www.enisa.europa.eu\/topics\/cyber-threats\/threat-landscape)"},{"key":"111878","unstructured":"4. Kafali E: Crafting a Tailored Threat Matrix for KINAITICS. CERTH;Jan. 2024. Reference Source (https:\/\/kinaitics.eu\/crafting-a-tailored-threat-matrix-for-kinaitics\/)"},{"key":"111879","unstructured":"5. MITRE Corporation: MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems. 2024. Reference Source (https:\/\/atlas.mitre.org)"},{"key":"111880","unstructured":"6. HIPAA Journal: Healthcare Data Breaches Due to Phishing. Sep. 2024. Reference Source (https:\/\/www.hipaajournal.com\/healthcare-data-breaches-due-to-phishing\/)"},{"key":"111881","unstructured":"7. Yeo LH, Banerjee A: Human Factors in Electronic Health Records Cybersecurity Breach: An Exploratory Analysis. J. Med. Internet Res. Mar. 2022."},{"key":"111882","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2004.1443014"},{"key":"111883","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2004.1443013"},{"key":"111884","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2005.1588117"},{"key":"111885","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2003.1291248"},{"key":"111886","unstructured":"12. Marcheschi P, Mazzarisi A, Dalmiani S, et al.: \u2018ECG standards for the interoperability in patient electronic health records in Italy\u2019, in. Comput. Cardiol. Sep. 2006; 2006: 549\u2013552. Reference Source (https:\/\/ieeexplore.ieee.org\/document\/4511910)"},{"key":"111887","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.2003.1291168"},{"key":"111888","unstructured":"14. Blanchard J-B, Damblin G, Martinez J-M: The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis. ArXiv Prepr. ArXiv180310656. Mar. 2018. Reference Source (https:\/\/arxiv.org\/abs\/1803.10656)"},{"key":"111889","unstructured":"15. Bluesight: Phishing Attacks: A Hacker\u2019s Gateway to Patient Health Records. Mar. 2025. Reference Source (https:\/\/bluesight.com\/news\/phishing-attacks-a-hackers-gateway-to-patient-health-records\/)"},{"key":"111890","unstructured":"16. Pawelczyk M, Di JZ: Machine Unlearning Fails to Remove Data Poisoning Attacks. ArXiv Prepr. ArXiv240617216. Jun. 2024. Reference Source (https:\/\/arxiv.org\/abs\/2406.17216)"},{"key":"111891","unstructured":"17. Nasim MAA, Biswas P: Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks. ArXiv Prepr. ArXiv240800348. Jul. 2024. Reference Source (https:\/\/arxiv.org\/abs\/2408.00348)"},{"key":"111892","unstructured":"18. Bunny.net: Bot Detection and Mitigation Techniques. 2024. Reference Source (https:\/\/bunny.net\/academy\/security\/bot-detection-and-mitigation-techniques\/)"},{"key":"111893","unstructured":"19. Radware: Role of Machine Learning in Thwarting Automated Bot Attacks. Oct. 2025. Reference Source (https:\/\/www.radware.com\/blog\/application-protection\/role-of-machine-learning-in-thwarting-automated-bot-attacks\/)"},{"key":"111894","doi-asserted-by":"publisher","DOI":"10.3390\/s24072115"},{"key":"111895","unstructured":"21. Encardio: AI in Civil Infrastructure and Structural Health Monitoring. 2024. Reference Source (https:\/\/www.encardio.com\/blog\/artificial-intelligence-civil-infrastructure-health-monitoring)"},{"key":"111896","unstructured":"22. Mittal A, Chowdhury T, Sivaraman R: Phishing Detection Using Natural Language Processing and Machine Learning. SMU Data Sci. Rev. 2022; 5(2)."},{"key":"111897","unstructured":"23. Geisler S, Wollschl\u00e4ger T: Attacking Large Language Models with Projected Gradient Descent. ArXiv Prepr. ArXiv240209154. Feb. 2024. Reference Source (https:\/\/arxiv.org\/abs\/2402.09154)"},{"key":"111898","unstructured":"24. Gu X, Fields G: Trojan Cleansing with Neural Collapse. ArXiv Prepr. ArXiv241112914. Dec. 2022. Reference Source (https:\/\/arxiv.org\/abs\/2411.12914)"},{"key":"111899","unstructured":"25. Kaggle: Understanding PGD Attacks in Deep Learning. Jan. 2024. Reference Source (https:\/\/www.kaggle.com\/general\/464481)"},{"key":"111900","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-02944-6"},{"key":"111901","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-94624-8"},{"key":"111902","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2025.05.006"},{"key":"111903","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110495"},{"key":"111904","unstructured":"30. IDMEFv2 Task Force: The Incident Detection Message Exchange Format version 2 (IDMEFv2). May 2023. Reference Source (https:\/\/idmefv2.github.io)"},{"key":"111905","unstructured":"31. ENISA: ENISA AI Threat Landscape Report. European Union Agency for Cybersecurity;Dec. 2020. Reference Source (https:\/\/www.enisa.europa.eu\/publications\/artificial-intelligence-cybersecurity-challenges)"},{"key":"111906","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-40813-y"},{"key":"111907","unstructured":"33. 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