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Neural Network Typing Authentication
TJ STAR Luke Knepper
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The Dilemma Passwords can sometimes be suboptimal
Advanced biometrics are expensive Need an alternative
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A Solution Authenticate people by how they type
Typing patterns differ by person user’s specific typing characteristics will be used for authentication Cheap and flexible to implement
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Background Measures users' typing patterns, compares to a previous standard Technique first used in WWII Works with ~90% Accuracy Effectively implemented in a neural network structure
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Background
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Development (front-end)
On account set-up, user types large amounts of text On subsequent log-ins, user types smaller amount of text Username and password are still needed
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Development (back-end)
Set-up data is used to train a neural network The node weights are tweaked thousands of times to train the neural network to output the desired result Log-in data will be fed through neural network: result either meets threshold (admitted) or does not meet (rejected)
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Experimentation Step 1:
Collected lots of test typing data via an online applet
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Experimentation Step 2:
Use this data to test the accuracy of the neural network approach: Train a neural network for each data file collected, run every other file through that neural network, record how many breach
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Experimentation Step 3:
Tweak the neural network set-up and repeat step 2 to find the optimal neural network
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Increased exposure (25 → 50)
Results Network Type Mean Training Time % Breached (threshold of .007) Base Network 1.1 s 90.5% Increased exposure (25 → 50) 2.3 s 90.7% Increased Cycles (1000 → 2000) 2.7 s 90.9% Increasing Layers (1 → 3) 3.1 s Increasing All 7.5 s
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Another Advantage What if another person jumps on the computer while you are logged in? Can continuously monitor the user's typing patterns during program use If a change is detected, system suspects an intruder and locks the user out
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Demo Let’s have some fun.
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Neural Network Typing Authentication
TJ STAR Luke Knepper
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