SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

Davide Cerotti, Daniele Codetta Raiteri, Giovanna Dondossola, Lavinia Egidi, Giuliana Franceschinis (), Luigi Portinale, Davide Savarro and Roberta Terruggia
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Davide Cerotti: Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
Daniele Codetta Raiteri: Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
Giovanna Dondossola: Transmission and Distribution Technologies Department, Ricerca sul Sistema Energetico (RSE S.p.A.), 20134 Milano, Italy
Lavinia Egidi: Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
Giuliana Franceschinis: Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
Luigi Portinale: Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
Davide Savarro: Computer Science Department, Università di Torino, 10149 Torino, Italy
Roberta Terruggia: Transmission and Distribution Technologies Department, Ricerca sul Sistema Energetico (RSE S.p.A.), 20134 Milano, Italy

Energies, 2024, vol. 17, issue 16, 1-30

Abstract: 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.

Keywords: cyberattack detection; cyber physical power systems; distributed energy resources; Bayesian Networks; risk assessment; attack graphs; MITRE ATT&CK framework; IEC 61850; evidence-based and time-driven probabilistic analysis; multiformalism models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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