User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain
Abstract
New and evolving threats emerge every day in the e-Health industry. The safety of e-Health’s telemonitoring systems is becoming a prominent task. In this work, starting from a CADS (Cyberattack Detection System) model that uses artificial intelligence techniques to detect anomalies, we focus on the activity of interacting with data. Using a User Interaction Engine, a dashboard allows you to visually explore and view data from suspected attacks on healthcare professionals for a threat reaction. In particular, a User Feedback module is presented to interact with healthcare personnel and ask for a response on the anomaly detected.
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