Acoustic Attack on Keyboard Using Spectrogram and Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU109598" target="_blank" >RIV/00216305:26220/14:PU109598 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7296341&tag=1" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7296341&tag=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2015.7296341" target="_blank" >10.1109/TSP.2015.7296341</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Acoustic Attack on Keyboard Using Spectrogram and Neural Network

Popis výsledku v původním jazyce

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 potential repeatability and feasibility of the proposed method.

Název v anglickém jazyce

Acoustic Attack on Keyboard Using Spectrogram and Neural Network

Popis výsledku anglicky

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 potential repeatability and feasibility of the proposed method.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/FR-TI4%2F647" target="_blank" >FR-TI4/647: *Integrační server s kryptografickým zabezpečením</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 38th International Conference on Telecommunication and Signal Processing

ISBN

9781479984978

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

637-641

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Berlín

Datum konání akce

1. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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