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Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1176-492
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7599-4367
2019 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 8, no 1, p. 213-216Article in journal (Refereed) Published
Abstract [en]

Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 8, no 1, p. 213-216
Keywords [en]
Adversarial attacks, Deep learning, Wireless security, Modulation classification, Neural networks.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-150945DOI: 10.1109/LWC.2018.2867459ISI: 000459510200053Scopus ID: 2-s2.0-85052663750OAI: oai:DiVA.org:liu-150945DiVA, id: diva2:1245700
Note

Funding agencies: ELLIIT, Security-Link; SURPRISE project - Swedish Foundation for Strategic Research (SSF)

Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-03-08Bibliographically approved

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Sadeghi, MeysamLarsson, Erik G.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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