Step-gan: A one-class anomaly detection model with applications to power system security

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Fältnamn	Indikatorer	Metadata
000		03237naa a2200541 4500
001		oai:DiVA.org:kth-311762
003		SwePub
008		220503s2021 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3117622 URI
024	7	a https://doi.org/10.1109/ICASSP39728.2021.94151022 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a kon2 swepub-publicationtype
100	1	a Adiban, M.4 aut
245	1 0	a Step-gan :b A one-class anomaly detection model with applications to power system security
264	1	b Institute of Electrical and Electronics Engineers (IEEE),c 2021
338		a print2 rdacarrier
500		a Part of proceedings: ISBN 978-1-7281-7605-5QC 20220503
520		a Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multigenerator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms the baseline system (OCAN) with about 55% in terms of accuracy on a freely available cyber attack dataset.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
653		a Anomaly Detection
653		a Cyber Attacks
653		a Mode Collapse
653		a One-Class Classification
653		a Power Systems
653		a Security
653		a Electric power system security
653		a Electric power transmission networks
653		a Security of data
653		a Signal processing
653		a Smart power grids
653		a Adversarial networks
653		a Anomaly detection models
653		a False data injection
653		a Low computational complexity
653		a Objective functions
653		a Power system security
653		a Smart grid systems
653		a Training procedures
700	1	a Safari, A.4 aut
700	1	a Salvi, Giampierou KTH,Tal, musik och hörsel, TMH4 aut0 (Swepub:kth)u12rf6rn
710	2	a KTHb Tal, musik och hörsel, TMH4 org
773	0	t ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsd : Institute of Electrical and Electronics Engineers (IEEE)g , s. 2605-2609q <2605-2609
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-311762
856	4 8	u https://doi.org/10.1109/ICASSP39728.2021.9415102

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Av författaren/redakt...: Adiban, M.; Safari, A.; Salvi, Giampiero

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