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sabato, 21 dicembre 2024

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Autori:Davide Cerotti
Daniele Codetta Raiteri
Giovanna Dondossola
Lavinia Egidi
Giuliana Franceschinis
Luigi Portinale
Davide Savarro
Roberta Terruggia
Area Scientifica:Uncertain Reasoning
Probabilistic Graphical Models
Computer Security
Dependability and Reliability
Titolo:SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems
Apparso su:Energies, vol. 17(16)
Editore:MDPI
Anno:2024
Tipo Pubblicazione:Paper on International Journal
URL:https://www.mdpi.com/1996-1073/17/16/3882
Sommario:SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study.