Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Mobile Networks and Applications

17.05.2024

Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios

verfasst von: Jibo Shi, Bin Ge, Qiong Wu, Ruichang Yang, Yan Sun

Erschienen in: Mobile Networks and Applications

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The number of terminal devices has skyrocketed along with the quick growth of cognitive radio networks. Massive equipment produce a lot of data that should not be shared, often WiFi signals. The radio frequency (RF) fingerprint identification approach for WiFi signals proposed in this research is based on federated learning and trains a collaborative model to complete RF fingerprint without transferring privacy-sensitive data. Aiming at the lack of labeled data and heterogeneous distribution of labeled data in actual situations, a federated transfer learning mechanism is designed. The technique suggested in this paper increases the accuracy of RF fingerprint at various sizes and assures that data privacy is not compromised, according to experimental results on real-world datasets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Lin Y, Zhao H, Ma X, Tu Y, Wang M (2020) Adversarial attacks in modulation recognition with convolutional neural networks. IEEE Trans Reliab 70(1):389–401CrossRef
2.
Sadeghi M, Larsson EG (2018) Adversarial attacks on deep-learning based radio signal classification. IEEE Wireless Communications Letters 8(1):213–216CrossRef
3.
Bhatt R, Maheshwary P, Shukla P, Shukla P, Shrivastava M, Changlani S (2020) Implementation of fruit fly optimization algorithm (ffoa) to escalate the attacking efficiency of node capture attack in wireless sensor networks (wsn). Comput Commun 149:134–145CrossRef
4.
Zhang J, Xie Y, Wu Q, Xia Y (2019) Medical image classification using synergic deep learning. Med Image Anal 54:10–19CrossRef
5.
Sorin V, Barash Y, Konen E, Klang E (2020) Deep learning for natural language processing in radiology-fundamentals and a systematic review. J Am Coll Radiol 17(5):639–648CrossRef
6.
Zhao, Y, Ge, L, Xie, H, Bai, G, Zhang, Z, Wei, Q, Lin, Y, Liu, Y, Zhou, F (2022)mAstf: visual abstractions of time-varying patterns in radio signals. IEEE Transactions on Visualization and Computer Graphics
7.
8.
O’Shea TJ, Roy T, Clancy TC (2018) Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing 12(1):168–179CrossRef
9.
Wang Y, Liu M, Yang J, Gui G (2019) Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Trans Veh Technol 68(4):4074–4077CrossRef
10.
Dong Y, Jiang X, Zhou H, Lin Y, Shi Q (2021) Sr2cnn: Zero-shot learning for signal recognition. IEEE Transactions on Signal Processing 69:2316–2329
11.
Hou, C, Liu, G, Tian, Q, Zhou, Z, Hua, L, Lin, Y (2022) Multi-signal modulation classification using sliding window detection and complex convolutional network in frequency domain. IEEE Internet of Things Journal
12.
Lin Y, Tu Y, Dou Z, Chen L, Mao S (2020) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking 7(1):34–46CrossRef
13.
Lin Y, Tu Y, Dou Z (2020) An improved neural network pruning technology for automatic modulation classification in edge devices. IEEE Trans Veh Technol 69(5):5703–5706CrossRef
14.
Ya T, Yun L, Haoran Z, ZHANG, J, Yu, W, Guan, G, Shiwen, M, (2022) Large-scale real-world radio signal recognition with deep learning. Chin J Aeronaut 35(9):35–48
15.
Lin, Y, Jia, J, Wang, S, Ge, B, Mao, S (2020) Wireless device identification based on radio frequency fingerprint features. In: ICC 2020-2020 IEEE international conference on communications (ICC), pp 1–6
16.
Peng L, Hu A, Zhang J, Jiang Y, Yu J, Yan Y (2018) Design of a hybrid rf fingerprint extraction and device classification scheme. IEEE Internet of Things Journal 6(1):349–360CrossRef
17.
Yin, P, Peng, L, Zhang, J, Liu, M, Fu, H, Hu, A (2021) Lte device identification based on rf fingerprint with multi-channel convolutional neural network. In: 2021 IEEE global communications conference (GLOBECOM), pp 1–6
18.
Merchant K, Revay S, Stantchev G, Nousain B (2018) Deep learning for rf device fingerprinting in cognitive communication networks. IEEE Journal of Selected Topics in Signal Processing 12(1):160–167CrossRef
19.
Youssef K, Bouchard L, Haigh K, Silovsky J, Thapa B, Vander Valk C (2018) Machine learning approach to rf transmitter identification. IEEE Journal of Radio Frequency Identification 2(4):197–205
20.
Xu, Z, Han, G, Liu, L, Zhu, H, Peng, J (2022) A lightweight specific emitter identification model for iiot devices based on adaptive broad learning. IEEE Transactions on Industrial Informatics
21.
Zhang, T, Mao, S (2022) An introduction to the federated learning standard. GetMobile: Mobile Computing and Communications 25(3):18–22
22.
Zhang W, Zhou T, Lu Q, Wang X, Zhu C, Sun H, Wang Z, Lo SK, Wang F-Y (2021) Dynamic-fusion-based federated learning for covid-19 detection. IEEE Internet of Things Journal 8(21):15884–15891CrossRef
23.
Zhou P, Wang K, Guo L, Gong S, Zheng B (2019) A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems. IEEE Trans Knowl Data Eng 33(3):824–838
24.
Liu M, Liu Z, Lu W, Chen Y, Gao X, Zhao N (2021) Distributed few-shot learning for intelligent recognition of communication jamming. IEEE Journal of Selected Topics in Signal Processing 16(3):395–405CrossRef
25.
Li Q, Fan H, Sun W, Li J, Chen L, Liu Z (2017) Fingerprints in the air: Unique identification of wireless devices using rf rss fingerprints. IEEE Sensors J 17(11):3568–3579CrossRef
26.
Li J, Qiu S, Shen Y-Y, Liu C-L, He H (2019) Multisource transfer learning for cross-subject eeg emotion recognition. IEEE Transactions on Cybernetics 50(7):3281–3293
27.
Wang Y, Gui G, Gacanin H, Ohtsuki T, Sari H, Adachi F (2020) Transfer learning for semi-supervised automatic modulation classification in zf-mimo systems. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10(2):231–239CrossRef
28.
Guo L, Lei Y, Xing S, Yan T, Li N (2018) Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans Ind Electron 66(9):7316–7325CrossRef
29.
Cheng Y, Lu J, Niyato D, Lyu B, Kang J, Zhu S (2022) Federated transfer learning with client selection for intrusion detection in mobile edge computing. IEEE Commun Lett 26(3):552–556CrossRef
30.
Cuturi, M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26
Metadaten
Titel
Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios
verfasst von
Jibo Shi
Bin Ge
Qiong Wu
Ruichang Yang
Yan Sun
Publikationsdatum
17.05.2024
Verlag
Springer US
Erschienen in
Mobile Networks and Applications
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-023-02229-0