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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | SecRiskAI: a Machine Learning-Based Approach for Cybersecurity Risk Prediction in Businesses |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | 24th IEEE International Conference on Business Informatics |
| Event Type | conference |
| Event Location | Amsterdam, Netherlands |
| Event Start Date | June 15 - 2022 |
| Event End Date | June 17 - 2022 |
| Place of Publication | Amsterdam, Netherlands |
| Publisher | IEEE |
| Abstract Text | Cyberattacks have increased in number and severity, negatively impacting businesses and their services. As such, cybersecurity can no longer be seen just as a technological issue, but it must also be recognized as critical to the economy and society. Current solutions struggle to find indicators of unpredictable risks, limiting their ability to perform accurate risk assessments. This work thus introduces SecRiskAI, an approach that employs Machine Learning (ML) to assess and predict how exposed a business is to cybersecurity risks. For this purpose, four ML algorithms were implemented, trained, and evaluated using synthetic datasets representing characteristics of different sizes of businesses (e.g., number of employees, business sector, and known vulnerabilities). Moreover, a Web-based user interface is provided to simplify the risk prediction workflow. The quantitative evaluation performed on SecRiskAI shows a minimal performance overhead and the high accuracy of the ML models, while a case study assesses the feasibility of the overall process for decision-makers. |
| Official URL | https://www.merlin.uzh.ch/contributionDocument/download/14680 |
| Digital Object Identifier | 10.1109/CBI54897.2022.00008 |
| Other Identification Number | merlin-id:23193 |
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