liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • text
  • asciidoc
  • rtf
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: 0000-0002-1176-4925
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: 2024-01-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Sadeghi, MeysamLarsson, Erik G.

Search in DiVA

By author/editor
Sadeghi, MeysamLarsson, Erik G.
By organisation
Communication SystemsFaculty of Science & Engineering
In the same journal
IEEE Wireless Communications Letters
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 283 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • text
  • asciidoc
  • rtf