An Artificial Intelligence Cyberattack Detection System to Improve Threat Reaction in e-Health
Carmelo Antonio Ardito, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Andrea Pazienza, Felice Vitulano · Zenodo (CERN European Organization for Nuclear Research) · 2021
In the e-Health domain, new and continuously evolving threats emerge every day. The security of e-Health telemonitoring systems is no longer negligible. In this paper, we propose a Cyberattack Detection System (CADS) model that exploits artificial intelligence techniques to detect anomalies without requiring a security analyst, explain the malicious activity, and display suspected attack data to healthcare personnel for feedback. The system description is contextualized to the case of the hacked remote patient health telemonitoring.