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Secur."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security-related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security-related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security-related information on Internet (e.g. cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions.<\/jats:p>","DOI":"10.1007\/s10207-023-00769-w","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T03:02:06Z","timestamp":1698375726000},"page":"31-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Cyber threat assessment and management for securing healthcare ecosystems using natural language processing"],"prefix":"10.1007","volume":"23","author":[{"given":"Stefano","family":"Silvestri","sequence":"first","affiliation":[]},{"given":"Shareful","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Dmitry","family":"Amelin","sequence":"additional","affiliation":[]},{"given":"Gabriele","family":"Weiler","sequence":"additional","affiliation":[]},{"given":"Spyridon","family":"Papastergiou","sequence":"additional","affiliation":[]},{"given":"Mario","family":"Ciampi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"769_CR1","unstructured":"Rees, D.: Cyber attacks in healthcare: the position across Europe (2021). https:\/\/www.pinsentmasons.com\/out-law\/analysis\/cyber-attacks-healthcare-europe"},{"key":"769_CR2","unstructured":"McKee, D., Laulheret, P.: McAfee Enterprise ATR uncovers vulnerabilities in globally used B. 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In: 2020 IEEE Symposium on Computers and Communications (ISCC), pp. 1\u20137 (2020). https:\/\/doi.org\/10.1109\/ISCC50000.2020.9219640","DOI":"10.1109\/ISCC50000.2020.9219640"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00769-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-023-00769-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-023-00769-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T20:16:45Z","timestamp":1705954605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-023-00769-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["769"],"URL":"https:\/\/doi.org\/10.1007\/s10207-023-00769-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3307666\/v1","asserted-by":"object"}]},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,27]]},"assertion":[{"value":"27 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors participated in project funded by the European Commission AI4HEALTHSEC\u2014A Dynamic and Self-Organised Artificial Swarm Intelligence Solution for Security and Privacy Threats in Healthcare ICT Infrastructures (grant number 883273). The article describes some of the results of this project.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants and\/or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical standards"}}]}}