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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n                    Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as \u201cIF the entrance\n                    <jats:italic>Netatmo Wheather Station<\/jats:italic>\n                    detects a temperature above 30\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({}^{\\circ}C\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(86^{\\circ}F\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) THEN open the shutters in the living room,\u201d might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub\n                    <jats:xref ref-type=\"fn\">\n                      <jats:sup>1<\/jats:sup>\n                    <\/jats:xref>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3675401","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T11:51:45Z","timestamp":1719661905000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3898-7512","authenticated-orcid":false,"given":"Bernardo","family":"Breve","sequence":"first","affiliation":[{"name":"University of Salerno, Fisciano (SA), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8061-7104","authenticated-orcid":false,"given":"Gaetano","family":"Cimino","sequence":"additional","affiliation":[{"name":"University of Salerno, Fisciano (SA), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-3590","authenticated-orcid":false,"given":"Vincenzo","family":"Deufemia","sequence":"additional","affiliation":[{"name":"University of Salerno, Fisciano (SA), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"242","volume-title":"Proceedings of the International Conference on Machine Learning (ICML \u201919)","author":"Allen-Zhu Zeyuan","year":"2019","unstructured":"Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 2019. 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