Sökning: id:"swepub:oai:DiVA.org:kth-311762" > Step-gan :
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 |
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.