CS 380S 0x1A Great Papers in Computer Security Vitaly Shmatikov http://www.cs.utexas.edu/~shmat/courses/cs380s/ L. Zhuang, F. Zhou, D. Tygar Keyboard Acoustic Emanations Revisited (CCS 2005) Acoustic Information in Typing Different keystrokes make different sounds Different locations on the supporting plate Each key is slightly different Frequency information in the sound of the typed key can be used to learn which key it is Observed by Asonov and Agrawal (2004) slide 3
Key Observation Build acoustic model for keyboard and typist Exploit the fact that typed text is nonrandom (for example, English) Limited number of words Limited letter sequences (spelling) Limited word sequences (grammar) This requires a language model Statistical learning theory Natural language processing slide 4 Sound of a Keystroke [Zhuang, Zhou, Tygar] Each keystroke is represented as a vector of Cepstrum features Fourier transform of the decibel spectrum Standard technique from speech processing slide 5 Bi-Grams of Characters [Zhuang, Zhou, Tygar]
Group keystrokes into N clusters Find the best mapping from cluster labels to characters Unsupervised learning: exploit the fact that some 2-character combinations are more common Example: th vs. tj Hidden Markov Models (HMMs) t 5 h 11 e 2 slide 6 Add Spelling and Grammar [Zhuang, Zhou, Tygar]
Spelling correction Simple statistical model of English grammar Tri-grams of words Use HMMs again to model slide 7 Recovered Text [Zhuang, Zhou, Tygar] Before spelling and grammar correction After spelling and grammar correction _____ = errors in recovery
= errors corrected by grammar slide 8 Feedback-based Training [Zhuang, Zhou, Tygar] Recovered characters + language correction provide feedback for more rounds of training Output: keystroke classifier Language-independent Can be used to recognize random sequence of keys For example, passwords Representation of keystroke classifier Neural networks, linear classification, Gaussian mixtures slide 9 Overview Initial
trainin g wave signal (recorded sound) Subsequen t recognition [Zhuang, Zhou, Tygar] wave signal Feature Extraction Feature Extraction Unsupervised Learning Keystroke Classifier Language Model Correction
Language Model Correction (optional) Sample Collector Classifier Builder keystroke classifier recovered keystrokes slide 10 Experiment: Single Keyboard [Zhuang, Zhou, Tygar] Logitech Elite Duo wireless keyboard 4 data sets recorded in two settings: quiet and noisy Consecutive keystrokes are clearly separable Automatically extract keystroke
positions in the signal with some manual error correction slide 11 Results for a Single Keyboard [Zhuang, Zhou, Tygar] Datasets Recording length Number of words Number of keys Set 1 ~12 min ~400 ~2500 Set 2
~27 min ~1000 ~5500 Set 3 ~22 min ~800 ~4200 Set 4 ~24 min ~700 ~4300 Initial and final recognition rate Set 1 (%)
Set 2 (%) Set 3 (%) Set 4 (%) Word Char Word Char Word Char Word Char Initial
35 76 39 80 32 73 23 68 Final 90 96 89
96 83 95 80 92 slide 12 Experiment: Multiple Keyboards [Zhuang, Zhou, Tygar] Keyboard 1: Dell QuietKey PS/2 In use for about 6 months Keyboard 2: Dell QuietKey PS/2 In use for more than 5 years Keyboard 3: Dell Wireless Keyboard New
slide 13 Results for Multiple Keyboards [Zhuang, Zhou, Tygar] 12-minute recording with app. 2300 characters Keyboard 1 (%) Keyboard 2 (%) Keyboard 3 (%) Word Char Word Char Word Char
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