A Behavioral Biometrics User Authentication Study - PowerPoint Presentation

Slide1

A Behavioral Biometrics User Authentication StudyUsing Android Device Accelerometer and Gyroscope Data

Jonathan Lee, Aliza Levinger, Beqir Simnica, Khushbu Kanani, Adam Pacholarz,

Javid Maghsoudi, Charles Tappert, Hui Zhao, and Leigh Anne Clevenger

(Preston Rollins, Vinnie Monaco)

Seidenberg School of CSIS, Pace University, Pleasantville, New YorkSlide2

CONTENTS

Tools Sensor Kinetics & WEKAData Gathering Protocol

Simple Processing

Data Processing

Results

CloseSlide3

TOOLS: SENSOR KINETICS & WEKA

Sensor Kinetics Pro

Access

all sensors

Record data

Share data

CSV File{time,x,y,z}1 line per sampleWEKACollection of Machine Learning AlgorithmsClassify DataTest OptionsEasily Change Algorithms

Naive Bayes

k-Nearest Neighbors

Multilayer PerceptronSlide4

DATA GATHERING PROTOCOL

Chose to record both

accelerometer

and

gyroscope

data - measures of device movement and orientation -- where available.

6 testers, 5 different android phones. 10 subjects per phone.Starting position had user sitting at desk/table with phone flat on the surface. Motion of interest consisted of:Press start button with dominant handLift to view2-3 second pauseBring phone to ear2-3 second pausePress stop button while phone is at earSlide5

SIMPLE PROCESSING

Divide the data into sixteen equal sectionsCalculate the average and variance at each data point

Divided into sixteenths to match up evenly with the automated feature extraction

16x sections

2x sensors

3x dimensions

2x values (avg, var)End with 192 data points per trialSlide6

Using 25-sample moving average to determine motionCalculates {x,y,z}-averages, derives variances at each point

Starts with an arbitrary threshold for motion

Iteratively adjusts threshold up/down

until system identifies 2 motions

and 2 periods of stillness

Divides each segment into quarters:

Calculate {x,y,z}-averages, variances192 data points per trial(mirroring the simple processing)DATA PROCESSINGSlide7

DATA PROCESSING CODE

Java code ~1000 linesPerforms simple sections (x16)Performs feature extraction

(4 segments)

Calculates avg/var per section

Generates 2x CSV filesSlide8

RESULTS

Initial test results with 60-individuals, on 6 different phones (5 models).

(acc) refers to analysis using only the accelerometer (96 data points)

(both) refers to analysis using the gyroscope as well (192 data points)

%

Simple (acc)

Simple (both)Complex (acc)Complex (both)Naive-Bayes82.783.281.183.6k-NN

86.3

87.088.7

89.8

MLP

91.4

92.5

92.9

92.7Slide9

SAMPLE RUN DATA

From complex extractionBoth Sensors

MLP

Individual data displayed (true pos & false pos

)

Some conclusions:

This biometric approach tricked by twinsAlso, possibly biomechanically similar individualsBehavioral biometrics alone may not be sufficientSlide10

COMPARISON TO PREVIOUS PROJECT

4 phones => 6 phones4 models => 5 models

20 users => 60 users

25 runs/user (500 runs total) => 20 runs/user (1200 runs total)

Standing motion => Sitting motion

Division into 8 sections => Division into 16 sections

Manual extraction + simple division => Simple division + automated extractionExtracted motions (manual) => Extracted motions & stillness (automated)98.4% result (both sensors/simple division/MLP) => 92.7% (both/complex/MLP)Slide11

3x Algorithms (naive Bayes, k-NN, MLP)Simple division vs Complex extraction

Accelerometer only vs. Both vs. Gyroscope only

Average and variance vs.

Average only

vs.

Variance only

10-fold validation vs. Leave 1 out validation60-sample vs. 50 vs. 40 vs. 30 vs. 20 vs. 10Aggregate vs. Individual12 Weka runs (~3 hrs) => 1296 Weka Runs (~308 hrs)PROJECT EXTENSION (BOLD NEW)