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

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  • Davide Cerotti

    (Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
    Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, Italy)

  • Daniele Codetta Raiteri

    (Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
    Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, 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
    Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, Italy)

  • Luigi Portinale

    (Computer Science Institute, DiSIT, Università del Piemonte Orientale (UPO), 15121 Alessandria, Italy
    Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, 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)

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.

Suggested Citation

  • Davide Cerotti & Daniele Codetta Raiteri & Giovanna Dondossola & Lavinia Egidi & Giuliana Franceschinis & Luigi Portinale & Davide Savarro & Roberta Terruggia, 2024. "SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems," Energies, MDPI, vol. 17(16), pages 1-30, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3882-:d:1451105
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    References listed on IDEAS

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    1. Pavlos Cheimonidis & Konstantinos Rantos, 2023. "Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review," Future Internet, MDPI, vol. 15(10), pages 1-25, September.
    2. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
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