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.
An artificial intelligence cyberattack detection system to improve threat reaction in e-Health / Ardito, C.; Di Noia, T.; Di Sciascio, E.; Lofù, D.; Pazienza, A.; Vitulano, F.. - 2940:(2021), pp. 270-283. (Intervento presentato al convegno 5th Italian Conference on Cybersecurity, ITASEC 2021 nel 2021).
An artificial intelligence cyberattack detection system to improve threat reaction in e-Health
Ardito C.;Di Noia T.;Di Sciascio E.;Lofù D.
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2021-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
