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An Efficient User Verification System via Mouse Movements

Paper presented by Nan Zheng, Aaron Paloski, Haining Wang from The College of William and Mary

Krithika Chandramouli

on 6 February 2014

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Transcript of An Efficient User Verification System via Mouse Movements

User authentication and verification are critical
Hijacked online sessions on social networks, online forums can be misused
Password theft
Secret questions- one time authentication

Ideal system: Accurate, Quick in response, Difficult to forge
Behavioral biometrics - 1999

Keystroke dynamics:
detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard

Graphical passwords:
user clicks on certain points on screen (an image is shown), and the sequence of clicks is the password
Pros and Cons
Static verification:
about 100 seconds for 20 clicks

Continuous verification:
an average of 1.89 mins for one click to happen => 37.73mins for verification
but 3.03 mins with partial movements included
Previous work
Image by Tom Mooring
An Efficient User Verification System
via Mouse Movements

why angle based metrics?
Platform independence
not affected by the OS, screen size, resolution, mouse
Distance: the accumulated deviation of two distributions over all intervals
Biometric Authentication
centered on "who you are"
a key or card can be lost or stolen
biometrics can neither be lost nor taken away from you
physiological: fingerprint, retina scan-
special hardware
behavioral: keystroke dynamics, mouse dynamics-
use human computer interaction (HCI) get data from common input devices like keyboard, mice etc
Mouse movement and characterization
Data collection through routine computing activities like surfing the net, playing games, chatting etc:
controlled set- 30 users from diverse backgrounds in a normal but controlled environment
field set: ~1000 users on an online forum over 1hr window
, t, x, y)
mouse move, mouse click

Angle Based Metrics
Direction for 2 points AB is the angle between AB and horizontal (x)
Angle of curvature for 3 points A,B,C is the angle between AB and BC (y)
Curvature distance for A,B,C is ratio of length(AC) to perpendicular from B to AC
similar for same users on different computers, different among different users
distance for a users curvature angles with himself and with others
System Architecture
record user's
compute angle-based
metrics from
raw data

threshold and majority
Implemented SVM using LIBSVM, an open sourced tool that can be integrated with Matlab, python,Ruby etc
output of SVM above threshold implies a decision of +1 => impostor
an output below threshold => decision -1 => true user
Choosing a majority
half of the samples belong to user, the other half to an 'impostor'
train the users profile 2
+1 times, with different sets
the final label is the one with majority
impostors classified as +1 and true users as -1
Evaluation of the system
Profiled on one platform and tested on the same platform
Decision making
Evaluation of the system
False Reject Rate FRR
a true user rejected as an impostor
False Accept Rate FAR
an impostor accepted as a true user
Block of clicks - point and click mouse actions
Verification time
Profiled on one platform and tested on another
innovative concept
well written paper
well thought of - performance evaluation, space consideration
"the verification system will be installed...."
including a snapshot of the UI
better structuring of the paper, maybe

Nan Zheng, Aaron Paloski, Haining Wang
Dept of Computer Science
The College of William and Mary
Full transcript