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Transcript of Sharif University of Technology Department of Computer Engineering Side Channel Attacks through...
Sharif University of Technology
Department of Computer Engineering
Side Channel Attacks through Acoustic Emanations
Presented by:
Amir Mahdi Hosseini Monazzah
Mohammad Taghi Teymoori
As:
Course Seminar of Hardware Security and Trust
Ord. 1393
Table of Contents
IntroductionPreliminaries
How FFT helps us!How Neural Network helps us!
Keyboard Acoustic EmanationsSimulation System Setup and ResultsConclusion and Future Work
Introd
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1 Side Channel Attacks through Acoustic Emanations
Electromagnetic Emanations
Attacks on the security of computer systemsElectromagnetic Emanations
Introd
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Prelim
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2 Side Channel Attacks through Acoustic Emanations
Optical Emanation
Attacks on the security of computer systemsOptical Emanation
Introd
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Prelim
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3 Side Channel Attacks through Acoustic Emanations
Acoustic Emanation
Attacks on the security of computer systemsAcoustic Emanation
Like the mentioned attacks, works on the pattern of (acoustic) signals
This attack is inexpensive and non-invasive!Only need a simple microphone.
Example attacks already implemented onDot matrix printersKeyboard
Introd
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Prelim
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4 Side Channel Attacks through Acoustic Emanations
How FFT Helps Us!
Fourier analysis converts time (or space) to frequency and vice versa.
FFT rapidly computes such transformations
Introd
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5 Side Channel Attacks through Acoustic Emanations
How FFT Helps Us! (Cont.)
The raw sound produced by key clicks is not a good input
We need to extract relevant features of sound
Introd
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6 Side Channel Attacks through Acoustic Emanations
How Neural Net. Helps Us!
Artificial neural network is a computational model capable of pattern recognition.
Classifies feature space
Data: set of value pairs: (xt, yt), yt=g(xt);
Objective: neural network represents the input / output transformation (a function) F
Learning: learning means using a set of observations to find F which solves the task in some optimal sense
Introd
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How Neural Net. Helps Us! (Cont.)
Introd
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.
Inputs
Outputw2
w1
w3
wn
wn-1.
x1
x2
x3
…
xn-1
xn
y)(;
1
zHyxwzn
iii
.
Attack Properties
Based on the hypothesis that the sound of clicks might differ slightly from key to keyAlthough the clicks of different keys sound similar
to the human ear
The network can be trained on one person and then used to eavesdrop on another person typing on the same keyboard
Introd
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Attack Properties (Cont.)
It is possible to train the network on one keyboard and then use it to attack another keyboard of the same typeThere is a reduction in the quality of recognition
The clicks sound different because the keys are positioned at different positions on the keyboard plate
Introd
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10 Side Channel Attacks through Acoustic Emanations
Signals Structure
The click lasts for approximately 100 msPeak of pushing the keySilencePeak of releasing the key
Introd
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Flow of Experiment
Introd
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Recording the sound of pressed key
Extract the push pick information
Calculating the FFT of push pick
Importing the information to neural network
Train the neural network with various redundant information
Test the neural network with random input
Success
Neural network trained
successfully
Create more accurate
information
No Yes
Motivational Example
Capturing the voice of pressing ‘h’ keyCapturing the voice of pressing ‘z’ key
Introd
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h
z
Motivational Example
Calculating the FFT of ‘h’ and ‘z’ signals
Introd
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14 Side Channel Attacks through Acoustic Emanations
h z
Push Peak
Silence
Release Peak
Motivational Example (Cont.)
Constructing the neural network and train it!
Error Prob.=8.87e-9
Introd
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MATLAB Code:…X=[Xz Xh];T=[0 1];net = newpr(X, T, 20);net = train(net, X, T);…
System Setup
Main PaperJava NNS neural network simulatorSimple PC microphone for short distances
up to 1 meter
Parabolic microphone for eavesdropping from a distance
IBM keyboard S/N 0953260, P/N 32P5100
Introd
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16 Side Channel Attacks through Acoustic Emanations
System Setup (Cont.)
This StudyMATLAB neural network simulatorSimple PC microphone for short distances
up to 1 meter
A4TECH keyboard model KR-85
Introd
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Results
Introd
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No Mistake
!
Constant Force :Variable Force :
Alice :Bob :
Victor :
Summary
We explored acoustic emanations of keyboardLike input devices to recognize the content being
typed
In the paper the attack was also applied toNotebook keyboardsTelephone padsATM pads
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Summary (Cont.)
A sound-free (non-mechanical) keyboard is an obvious countermeasure for the attackHowever, it is neither comfortable for users nor
cheap!
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20 Side Channel Attacks through Acoustic Emanations
Future Work
Main Idea:Improving the accuracy of the results by using the
combination of keyboard acoustic emanations and predictive text algorithms.
Introd
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21 Side Channel Attacks through Acoustic Emanations
Recording Acoustic
Emanation of Keyboard
Training Neural
Network
Activating the Eavesdropping
System
Processing the Results with
Predictive Text Algorithms
Generating the Text Result
22 Side Channel Attacks through Acoustic Emanations
Thanks for your attention
References
1. Asonov, Dmitri, and Rakesh Agrawal. "Keyboard acoustic emanations." In IEEE Symposium on Security and Privacy, vol. 2004, pp. 3-11. 2004.
2. Backes, Michael, Markus Dürmuth, Sebastian Gerling, Manfred Pinkal, and Caroline Sporleder. "Acoustic Side-Channel Attacks on Printers." In USENIX Security Symposium, pp. 307-322. 2010.
3. Kuhn, Markus G. "Optical time-domain eavesdropping risks of CRT displays." In Security and Privacy, 2002. Proceedings. 2002 IEEE Symposium on, pp. 3-18. IEEE, 2002.
4. Vuagnoux, Martin, and Sylvain Pasini. "Compromising Electromagnetic Emanations of Wired and Wireless Keyboards." In USENIX Security Symposium, pp. 1-16. 2009.
Introd
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