Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Design and Development of a Robotic Arm for Rehabilitation and Training Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Anomaly Detection of Spectrum in Wireless Communication via Deep Autoencoder

Authors : Qingsong Feng, Zheng Dou, Chunmei Li, Guangzhen Si

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Anomaly detection has been a typical task in many fields, as well as spectrum monitoring in wireless communication. In this paper, we apply a deep-structure autoencoder neural network to spectrum anomaly detection, and the time-frequency diagram is used as the feature of the learning model. In order to evaluate the performance of the model, the accuracy of the output is considered. We compare the performance of both our proposed model and conventional one-layer autoencoder. The results of numerical experiments illustrate that our model outperforms the one-layer autoencoder based method.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Generalized Multi-linear Mixed Effects Model
next chapter A Modified Complex ICA for Blind Source Separation and the Application in Communication Reconnaissance
Literature
1.
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection. ACM Comput. Surv. 41, 1–58 (2009)CrossRef
2.
Aleskerov, E., Freisleben, B., Cardwatch, R.B.: A neural network based database mining system for credit card fraud detection. In: Proceedings of the IEEE/IAFE 1997: IEEE Computational Intelligence for Financial Engineering (CIFEr), pp. 220–226 (1997)
3.
Kumar, V.: Parallel and distributed computing for cybersecurity. IEEE Distrib. Syst. Online, 1 (2005)
4.
Bertini, M., Del Bimbo, A., Seidenari, L.: Multi-scale and real-time non-parametric approach for anomaly detection and localization. Comput. Vis. Image Understand. 116, 320–329 (2012)CrossRef
5.
Khazai, S., Safari, A., Mojaradi, B., Homayouni, S.: An approach for subpixel anomaly detection in hyperspectral images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 6, 769–778 (2013)CrossRef
6.
Quinn, J.A., Sugiyama, M.: A least-squares approach to anomaly detection in static and sequential data. Pattern Recogn. Lett. 40, 36–40 (2014)CrossRef
7.
Marchi, E., Vesperini, F., Eyben, F., Squartini, S., Schuller, B.: A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1996–2000. IEEE (2015)
8.
Marchi E, Vesperini F, Weninger F, Eyben F, Squartini S, Schuller B., Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2015)
Metadata
Title
Anomaly Detection of Spectrum in Wireless Communication via Deep Autoencoder
Authors
Qingsong Feng
Zheng Dou
Chunmei Li
Guangzhen Si
Copyright Year
2017
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-3022-2

Electronic ISBN: 978-981-10-3023-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-3023-9_42