Computer security compromised by hardware failure: Difference between revisions

Revision as of 19:28, 16 January 2017

Revision as of 19:30, 16 January 2017

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With acoustic emanations, an attack that recovers what a dot-matrix printer processing English text is printing is possible. It is based on a record of the sound the printer makes, if the microphone is close enough to it. This attack recovers up to 72% of printed words, and up to 95% if knowledge about the text are done, with a microphone at a distance of 10&nbsp;cm from the printer.<ref name="[Back10]">[[#Back1|Backes, 2010, p.1]]</ref>

After an upfront training phase ("a" in the picture below), the attack ("b" in the picture below) is fully automated and uses a combination of machine learning, audio processing, and speech recognition techniques, including spectrum features, Hidden Markov Models and linear classification.<ref name="[Back2Back1]">[[#Back1]]</ref> The fundamental reason why the reconstruction of the printed text works is that, the emitted sound becomes louder if more needles strike the paper at a given time.<ref name="[Back2]"/> There is a correlation between the number of needles and the intensity of the acoustic emanation.<ref name="[Back2]">[[#Back1|Backes, 2010, p.2]]</ref>

A training phase was conducted where words from a dictionary are printed and characteristic sound features of these words are extracted and stored in a database. The trained characteristic features was used to recognize the printed English text.<ref name="[Back2]"/> But, this task is not trivial. Major challenges include :