Publication detail

Acoustic Attack on Keyboard Using Spectrogram and Neural Network

MARTINÁSEK, Z. ČLUPEK, V. TRÁSY, K.

Original Title

Acoustic Attack on Keyboard Using Spectrogram and Neural Network

Czech Title

Útok akustickým postranním kanálem využívající NN

English Title

Acoustic Attack on Keyboard Using Spectrogram and Neural Network

Type

conference paper

Language

en

Original Abstract

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.

Czech abstract

Akustický postranní kanál patří k nejstarším postranním kanálům. V dnešní době se útoky akustickým postranním kanálem zaměřují na klávesnice osobního počítače, bankomatů a vnitřních počítačových komponent. Pro zpětnou klasifikaci naměřených akustických průběhů jsou používány různé metody, které se primárně liší v oblasti zpracování naměřených průběhů. Dvě základní oblasti jsou časová oblast a frekvenční oblast. V našem útoku jsme se rozhodli pro kompromis a používáme spektrogram jako vstupní frekvenčně časovou oblast pro klasickou dvouvrstvou MLP. Návrh metody jsme experimentálně ověřili pomocí běžně dostupného zařízení (laptop s integrovaným mikrofonem).

English abstract

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.

Keywords

Side channels, acoustic analysis, classification, neural network.

RIV year

2014

Released

04.07.2014

ISBN

978 1 4799 8497 8

Book

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

Pages from

637

Pages to

641

Pages count

5

URL

BibTex


@inproceedings{BUT108311,
  author="Zdeněk {Martinásek} and Vlastimil {Člupek} and Krisztina {Trásy}",
  title="Acoustic Attack on Keyboard Using Spectrogram and Neural Network",
  annote="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.",
  booktitle="Proceedings of the 38th International Conference on Telecommunication and Signal Processing",
  chapter="108311",
  doi="10.1109/TSP.2015.7296341",
  howpublished="electronic, physical medium",
  year="2014",
  month="july",
  pages="637--641",
  type="conference paper"
}