Team for Research in
Ubiquitous Secure Technology

Keyboard Acoustic Emanations Revisited
Li Zhuang, Feng Zhou, J. D. Tygar

Citation
Li Zhuang, Feng Zhou, J. D. Tygar. "Keyboard Acoustic Emanations Revisited". To appear in Proceedings of the 12th ACM Conference on Computer and Communications Security, November 2005.

Abstract
We examine the problem of keyboard acoustic emanations. We present a novel attack taking as input a 10-minute sound recording of a user typing English text using a keyboard, and then recovering up to 96% of typed characters. There is no need for a labeled training recording. Moreover the recognizer bootstrapped this way can even recognize random text such as passwords: In our experiments, 90% of 5-character random passwords using only letters can be generated in fewer than 20 attempts by an adversary; 80% of 10- character passwords can be generated in fewer than 75 attempts. Our attack uses the statistical constraints of the underlying content, English language, to reconstruct text from sound recordings without any labeled training data. The attack uses a combination of standard machine learning and speech recognition techniques, including cepstrum features, Hidden Markov Models, linear classification, and feedback-based incremental learning.

Categories and Subject Descriptors: K.6.5 Security and Protection: Unauthorized access; K.4.1 Public Policy Issues: Privacy General Terms: Security

Keywords: Computer Security, Human Factors, Acoustic Emanations, Learning Theory, Hidden Markov Models, HMM, Cepstrum, Signal Analysis, Keyboards, Privacy, Electronic Eavesdropping

Electronic downloads

Citation formats  
  • HTML
    Li Zhuang, Feng Zhou, J. D. Tygar. <a
    href="http://www.truststc.org/pubs/3.html"
    >Keyboard Acoustic Emanations Revisited</a>,
    <i>To appear in Proceedings of the 12th ACM Conference
    on Computer and Communications Security</i>, November
    2005.
  • Plain text
    Li Zhuang, Feng Zhou, J. D. Tygar. "Keyboard Acoustic
    Emanations Revisited". <i>To appear in
    Proceedings of the 12th ACM Conference on Computer and
    Communications Security</i>, November 2005.
  • BibTeX
    @article{ZhuangZhouTygar05_KeyboardAcousticEmanationsRevisited,
        author = {Li Zhuang, Feng Zhou, J. D. Tygar},
        title = {Keyboard Acoustic Emanations Revisited},
        journal = {To appear in Proceedings of the 12th ACM
                  Conference on Computer and Communications Security},
        month = {November},
        year = {2005},
        abstract = {We examine the problem of keyboard acoustic
                  emanations. We present a novel attack taking as
                  input a 10-minute sound recording of a user typing
                  English text using a keyboard, and then recovering
                  up to 96% of typed characters. There is no need
                  for a labeled training recording. Moreover the
                  recognizer bootstrapped this way can even
                  recognize random text such as passwords: In our
                  experiments, 90% of 5-character random passwords
                  using only letters can be generated in fewer than
                  20 attempts by an adversary; 80% of 10- character
                  passwords can be generated in fewer than 75
                  attempts. Our attack uses the statistical
                  constraints of the underlying content, English
                  language, to reconstruct text from sound
                  recordings without any labeled training data. The
                  attack uses a combination of standard machine
                  learning and speech recognition techniques,
                  including cepstrum features, Hidden Markov Models,
                  linear classification, and feedback-based
                  incremental learning. <p>Categories and Subject
                  Descriptors: K.6.5 Security and Protection:
                  Unauthorized access; K.4.1 Public Policy Issues:
                  Privacy General Terms: Security <p>Keywords:
                  Computer Security, Human Factors, Acoustic
                  Emanations, Learning Theory, Hidden Markov Models,
                  HMM, Cepstrum, Signal Analysis, Keyboards,
                  Privacy, Electronic Eavesdropping },
        URL = {http://www.truststc.org/pubs/3.html}
    }
    

Posted by Christopher Brooks on 16 Sep 2005.
Groups: trust
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