PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Acoustic side-channel attacks on printers
Michael Backes, Markus Duermuth, Sebastian Gerling, Manfred Pinkal and Caroline Sporleder
In: 19th USENIX Security Symposium, 11-13 August 2010, Washington, DC.

Abstract

Abstract We examine the problem of acoustic emanations of print- ers. We present a novel attack that recovers what a dot- matrix printer processing English text is printing based on a record of the sound it makes, if the microphone is close enough to the printer. In our experiments, the at- tack recovers up to 72 % of printed words, and up to 95 % if we assume contextual knowledge about the text, with a microphone at a distance of 10cm from the printer. After an upfront training phase, the attack is fully auto- mated and uses a combination of machine learning, au- dio processing, and speech recognition techniques, in- cluding spectrum features, Hidden Markov Models and linear classification; moreover, it allows for feedback- based incremental learning. We evaluate the effective- ness of countermeasures, and we describe how we suc- cessfully mounted the attack in-field (with appropriate privacy protections) in a doctor’s practice to recover the content of medical prescriptions.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7116
Deposited By:Caroline Sporleder
Deposited On:04 March 2011