SUSAN: a deep learning based anomaly detection framework for sustainable industry

Perales Gómez, Ángel Luis; Fernández Maimó, Lorenzo; García Clemente, Félix J.; Huertas Celdrán, Alberto

Publication:
SUSAN: a deep learning based anomaly detection framework for sustainable industry

dc.contributor.author	Perales Gómez, Ángel Luis
dc.contributor.author	Fernández Maimó, Lorenzo
dc.contributor.author	García Clemente, Félix J.
dc.contributor.author	Huertas Celdrán, Alberto
dc.contributor.department	Ingeniería y Tecnología de Computadores
dc.contributor.editor	Elsevier
dc.date.accessioned	2024-06-28T08:11:32Z
dc.date.available	2024-06-28T08:11:32Z
dc.date.issued	2023-01-05
dc.description	© 2023 The Authors. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Published version of a Published Work that appeared in final form in Sustainable Computing: Informatics and Systems (SUSCOM). To access the final edited and published work see https://doi.org/10.1016/j.suscom.2022.100842
dc.description.abstract	Nowadays, sustainability is the core of green technologies, being a critical aspect in many industries concerned with reducing carbon emissions and energy consumption optimization. While this concern increases, the number of cyberattacks causing sustainability issues in industries also grows. These cyberattacks impact industrial systems that control and monitor the right functioning of processes and systems. Furthermore, they are very specialized, requiring knowledge about the target industrial processes, and being undetectable for traditional cybersecurity solutions. To overcome this challenge, we present SUSAN, a Deep Learning-based framework, to build anomaly detectors that expose cyberattacks affecting the sustainability of industrial systems. SUSAN follows a modular and flexible design that allows the ensembling of several detectors to achieve more precise detections. To demonstrate the feasibility of SUSAN, we implemented the framework in a water treatment plant using the SWaT testbed. The experiments performed achieved the best recall rate (0.910) and acceptable precision (0.633), resulting in an F1-score of 0.747. Regarding individual cyberattacks that impact the system’s sustainability, our implementation detected all of them, and, concerning the related work, it achieved the most balanced results, with 0.64 as the worst recall rate. Finally, a false-positive rate of 0.000388 makes our solution feasible in real scenarios.	es
dc.format	application/pdf	es
dc.format.extent	14
dc.identifier.citation	Sustainable Computing: Informatics and Systems (SUSCOM), 2023, Vol. 37: 100842
dc.identifier.doi	https://doi.org/10.1016/j.suscom.2022.100842
dc.identifier.issn	Print: 2210-5379
dc.identifier.issn	Electronic: 2210-5387
dc.identifier.uri	http://hdl.handle.net/10201/142733
dc.language	eng	es
dc.relation	This work has been funded under Grant TED2021-129300B-I00, by MCIN/AEI/10.13039/501100011033, NextGenerationEU/PRTR, UE, Grant PID2021-122466OB-I00 and Grant RTI2018-095855-B-I00, by MCIN/AEI/10.13039/501100011033/FEDER, UE, and the Swiss Federal Office for Defense Procurement (armasuisse) with the CyberSpec (CYD-C-2020003).	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S2210537922001731?via%3Dihub
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Anomaly detection
dc.subject	Deep learning
dc.subject	Industrial control systems
dc.subject	Machine learning
dc.subject	Sustainability
dc.title	SUSAN: a deep learning based anomaly detection framework for sustainable industry	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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