Long Text Keystroke Biometrics Study

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Long Text Keystroke Biometrics Study

• Gary Bartolacci, Mary Curtin, Marc Katzenberg,
  Ngozi Nwana
• Sung-Hyuk Cha, Charles Tappert
• (Software Engineering Project Team DPS Student)
  

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Keystroke Biometric

• Biometrics important for security apps
• Advantage - inexpensive and easy to implement,
  the only hardware needed is a keyboard
• Disadvantage - behavioral rather than
  physiological biometric, easy to disguise
• One of the least studied biometrics, thus good
  for dissertation studies

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Focus of Study

• Previous studies mostly concerned with short
  character string input
• Password hardening
• Short name strings
• We focus on large text input
• 200 or more characters per sample

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Focus of Study (cont)

• Applications of interest
• Identification
• 1-of-n classification problem
• e.g., sender of inappropriate e-mail in a
  business environment with a limited number of
  employees
• Verification
• Binary classification problem, yes/no
• e.g., student taking online exam

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Software Components

• Raw Keystroke Data Capture over the Internet
  (Java applet)
• Feature Extraction (SAS software)
• Classification (SAS software)
• Training
• Testing

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Keystroke Data Capture(Java Applet)

• Raw data recorded for each entry
• Keys character
• Keys code text equivalent
• Keys location on keyboard
• 1 standard, 2 left, 3 right
• Time key was pressed (msec)
• Time key was released (msec)
• Number of left, right, double mouse clicks

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Keystroke Data Capture(Java Applet)
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Aligned Raw Data File(Hello World!)
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Feature Extraction

• 10 Mean and 10 Std of key press durations
• 8 most frequent alphabet letters (e, a, r, i, o,
  t, n, s)
• Space shift keys
• 10 Mean and 10 Std of key transitions
• 8 most common digrams (in, th, ti, on, an, he,
  al, er)
• Space-to-any-letter any-letter-to-space
• 18 Total number of keypresses for
• Space, backspace, delete, insert, home, end,
  enter, ctrl, 4 arrow keys, shift (left), shift
  (right), total entry time, left, right, double
  mouse clicks

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Feature Extraction Preprocessing

• Outlier removal
• Remove samples gt 2 std from mean
• Prevents skewing of feature measurements caused
  by pausing of the keystroker
• Standardization
• x (x - xmin) / (xmax - xmin)
• Scales to range 0-1 to give roughly equal weight
  to each feature

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Sample Datasets
Prior to Standardization
After Standardization
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Classification

• Identification
• Nearest neighbor classifier using Euclidean
  distance
• Input sample compared to every training sample

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Experimental DesignIdentification Experiment

• 8 subjects that know the purpose of exp.
• Training 10 reps of text a (approx. 600 char)
• Testing
• 10 reps of text a
• 10 reps of text b (same length as text a)
• 10 reps of text c (half length of text a)

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Experimental Design Instructions for Subjects

• Subjects were told to input the data using their
  normal keystroke dynamics
• Subjects were asked leave at least a day between
  entering samples

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Experimental DesignText a about 600 characters

• This is an Aesop fable about the bat and the
  weasels. A bat who fell upon the ground and was
  caught by a weasel pleaded to be spared his life.
  The weasel refused, saying that he was by nature
  the enemy of all birds. The bat assured him that
  he was not a bird, but a mouse, and thus was set
  free. Shortly afterwards the bat again fell to
  the ground and was caught by another weasel, whom
  he likewise entreated not to eat him. The weasel
  said that he had a special hostility to mice. The
  bat assured him that he was not a mouse, but a
  bat, and thus a second time escaped. The moral of
  the story it is wise to turn circumstances to
  good account.

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Expected Outcomes Recognition Accuracy

• Accuracy on text a gt that on text b
• text a is the training text
• Accuracy on text b gt that on text c
• text b is longer than text c
• Accuracy on texts a, b, c gt arbitrary text
• texts a, b, c are similar, all Aesop fables

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Preliminary Results Reduced Experiment

• Reduced identification experiment
• Smaller text input
• The quick brown fox jumps over the lazy dog.
• Fewer subjects
• Three project team members
• Fewer feature measurements
• Mean and std for e and o key press durations
• Accuracy of 80, which is promising

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Results Comparison to Same Text
Predicted

• Prior to Standardization only yielded a 59
  accuracy
• 100 accuracy with standardization
• (76 out of 76)
• Confusion Matrix of Results after Standardization
  ?

Actual
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Results Comparison to Different Text of Equal
Length
Predicted

• Prior to Standardization only yielded a 38
  accuracy
• 98.5 accuracy with standardization
• (65 out of 66)
• Confusion Matrix of Results after Standardization
  ?

Actual
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Results Comparison to Different Text of Shorter
Length
Predicted

• Prior to Standardization only yielded a 14
  accuracy
• 97 accuracy with standardization
• (74 out of 76)
• Confusion Matrix of Results after Standardization
  ?

Actual
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Conclusions

• System is a viable means of differentiating
  between individuals based on typing patterns
• Standardization is crucial to the accuracy of the
  system
• It is likely that the shorter the text used for
  verification, the lower the accuracy
• Decreasing measurements used also decreases
  accuracy

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Questions/Comments?

• Focus or applications?
• Software implementation?
• Experimental design?
• Expected experimental outcomes?