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Article

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

by
Davide Cerotti
1,2,
Daniele Codetta Raiteri
1,2,
Giovanna Dondossola
3,
Lavinia Egidi
1,
Giuliana Franceschinis
1,2,*,
Luigi Portinale
1,2,
Davide Savarro
4 and
Roberta Terruggia
3
1
Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
2
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, Italy
3
Transmission and Distribution Technologies Department, Ricerca sul Sistema Energetico (RSE S.p.A.), 20134 Milano, Italy
4
Computer Science Department, Università di Torino, 10149 Torino, Italy
*
Author to whom correspondence should be addressed.
Energies 2024, 17(16), 3882; https://doi.org/10.3390/en17163882
Submission received: 8 June 2024 / Revised: 19 July 2024 / Accepted: 1 August 2024 / Published: 6 August 2024
(This article belongs to the Special Issue Model Predictive Control-Based Approach for Microgrids)
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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 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

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MDPI and ACS Style

Cerotti, D.; Codetta Raiteri, D.; Dondossola, G.; Egidi, L.; Franceschinis, G.; Portinale, L.; Savarro, D.; Terruggia, R. SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems. Energies 2024, 17, 3882. https://doi.org/10.3390/en17163882

AMA Style

Cerotti D, Codetta Raiteri D, Dondossola G, Egidi L, Franceschinis G, Portinale L, Savarro D, Terruggia R. SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems. Energies. 2024; 17(16):3882. https://doi.org/10.3390/en17163882

Chicago/Turabian Style

Cerotti, Davide, Daniele Codetta Raiteri, Giovanna Dondossola, Lavinia Egidi, Giuliana Franceschinis, Luigi Portinale, Davide Savarro, and Roberta Terruggia. 2024. "SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems" Energies 17, no. 16: 3882. https://doi.org/10.3390/en17163882

APA Style

Cerotti, D., Codetta Raiteri, D., Dondossola, G., Egidi, L., Franceschinis, G., Portinale, L., Savarro, D., & Terruggia, R. (2024). SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems. Energies, 17(16), 3882. https://doi.org/10.3390/en17163882

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