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Description
  • Acoustic side channel belongs to one of the oldest side channel and currently, the acoustic attacks are focused on computer keyboards, automated teller machine and internal computer components. Different methods are used for a classification of acoustic traces measured. It primary depends on the fact if the attacker processes the measured data in time or frequency domain. These two approaches use mostly neural networks connected to dictionary using hidden Markov models for an improvement of classification results. We decided for a compromise between the time and frequency domains and we process acoustic trace measured in the time-frequency domain by using a spectrogram. We use the spectrogram as an input of a typical two-layer neural network with the back propagation learning algorithm. This approach is based on a simple algorithm and does not use any other tool to improve classification results. We used widely available laptop with an integrated microphone placed in an office to analyze the potentia
  • Acoustic side channel belongs to one of the oldest side channel and currently, the acoustic attacks are focused on computer keyboards, automated teller machine and internal computer components. Different methods are used for a classification of acoustic traces measured. It primary depends on the fact if the attacker processes the measured data in time or frequency domain. These two approaches use mostly neural networks connected to dictionary using hidden Markov models for an improvement of classification results. We decided for a compromise between the time and frequency domains and we process acoustic trace measured in the time-frequency domain by using a spectrogram. We use the spectrogram as an input of a typical two-layer neural network with the back propagation learning algorithm. This approach is based on a simple algorithm and does not use any other tool to improve classification results. We used widely available laptop with an integrated microphone placed in an office to analyze the potentia (en)
Title
  • Acoustic Attack on Keyboard Using Spectrogram and Neural Network
  • Acoustic Attack on Keyboard Using Spectrogram and Neural Network (en)
skos:prefLabel
  • Acoustic Attack on Keyboard Using Spectrogram and Neural Network
  • Acoustic Attack on Keyboard Using Spectrogram and Neural Network (en)
skos:notation
  • RIV/00216305:26220/14:PU109598!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 1468
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/14:PU109598
http://linked.open...riv/jazykVysledku
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  • Side channels, acoustic analysis, classification, neural network. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [041FE1538A2E]
http://linked.open...v/mistoKonaniAkce
  • Berlín
http://linked.open...i/riv/mistoVydani
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • 37th International Conference on Telecommunications and Signal Processing TSP
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Martinásek, Zdeněk
  • Trásy, Krisztina
  • Člupek, Vlastimil
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 978-80-214-4983-1
http://localhost/t...ganizacniJednotka
  • 26220
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