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Lookup NU author(s): Luca Arnaboldi, Dr Ricardo Melo CzeksterORCiD, Dr Charles Morisset, Dr Roberto MetereORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Cyber-Physical Systems (CPS) are present in many settings addressing a myriad of purposes. Examples are Internet-of-Things (IoT) or sensing software embedded in appliances or even specialised meters that measure and respond to electricity demands in smart grids. Due to their pervasive nature, they are usually chosen as recipients for larger scope cyber-security attacks. Those promote system-wide disruptions and are directed towards one key aspect such as confidentiality, integrity, availability or a combination of those characteristics. Our paper focuses on a particular and distressing attack where coordinated malware infected IoT units are maliciously employed to synchronously turn on or off high-wattage appliances, affecting the grid's primary control management. Our model could be extended to larger (smart) grids, Active Buildings as well as similar infrastructures. Our approach models Coordinated Load-Changing Attacks (CLCA) also referred as GridLock or BlackIoT, against a theoretical power grid, containing various types of power plants. It employs Continuous-Time Markov Chains where elements such as Power Plants and Botnets are modelled under normal or attack situations to evaluate the effect of CLCA in power reliant infrastructures. We showcase our modelling approach in the scenario of a power supplier (e.g. power plant) being targeted by a botnet. We demonstrate how our modelling approach can quantify the impact of a botnet attack and be abstracted for any CPS system involving power load management in a smart grid. Our results show that by prioritising the type of power-plants, the impact of the attack may change: in particular, we find the most impacting attack times and show how different strategies impact their success. We also find the best power generator to use depending on the current demand and strength of attack.
Author(s): Arnaboldi L, Czekster RM, Morisset C, Metere R
Publication type: Article
Publication status: Published
Journal: Electronic Notes in Theoretical Computer Science
Year: 2020
Volume: 353
Pages: 39-60
Print publication date: 01/11/2020
Online publication date: 10/11/2020
Acceptance date: 30/09/2020
Date deposited: 12/11/2020
ISSN (print): 1571-0661
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.entcs.2020.09.018
DOI: 10.1016/j.entcs.2020.09.018
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