Cyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided.
Baldoni, S., Celozzi, G., Neri, A., Carli, M., Battisti, F. (2021). Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.67-76). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-69781-5_5].
Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems
Baldoni S.;Celozzi G.;Neri A.;Carli M.;Battisti F.
2021-01-01
Abstract
Cyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
