Unobtrusive Mobile User Recognition Patent by Seal Mobile ID Presented By: Aparna Bharati & Ashrut Bhatia

Motivation  Problem with passwords  Disadvantage of biometrics  Advanced HCI methods  Why not voice, gait and keystrokes ?

Introduction  Unobtrusively recognizing a user of a mobile device  Modern mobile devices are equipped with multiple sensors  Monitor HCI behavior patterns and combine to create “usage repertoires” that reflect unique behavior patterns of a specific individual user

Terminology 1.Motion State  Placement and speed determined from sensors 2.Motion Sequence  Collection of motion data extracted over period of time 3.Motion Characteristics  Aggregation of motion data in each motion sequence

Motion States

Terminology (contd.) 4.Motion Repertoire  Collection of motion characteristics captured while authorized usage 5.Differentiation Template  Set of motion characteristics that best differentiate user from population 6.Learning Stage Parameters  Trigger a defensive action/authenticate upon detecting unidentified motion-characteristics

Generating Differentiation Template  Calculate motion-characteristics of a plurality of users  Generate a differentiation score by comparing f(occurrence of each motion-characteristic of the user) to f(occurrence of the equivalent motion- characteristic of the population)  Generate differentiation-template for each user based on the differentiation scores

Continuous Monitoring Monitor Touch Events Touch Events Sample Touch Event Parameters Compare Motion Characteristics Discretize Parameters Motion Sequence Consistent with Prior usage Initial Learning mode Block Access Update Bayesian network Yes No Yes

Bayesian Network (basic)

Bayesian Network

Application of Machine Learning  Authorization - Compare motion-characteristics to a differentiation-template to recognize an authorized user:  For each motion-sequence, score each of the 16 characteristics based on the probability score in the differentiation-template.  Detection Score = ∑ 16 characteristic scores.  High score indicates the likelihood of the authorized user.

Other Statistical Techniques  Entropy method:  Chi-Squared Test:

User Recognition Collecting Motion Data from handheld device Determine motion state Demarcate series into motion sequence Calculate motion characteristics of auth. user Generate motion-repertoire of auth. user Determine motion data of new user Calculate motion char. of new user Compare current char. to motion repertoire of auth. user Trigger defense Provide authentication

Performance Evaluation  The accuracy is measured using FAR and FRR  System dynamically scales itself to provide balance b/w accuracy and device performance:  Time taken by calls to the server is determined  Time Field within server response indicating the time for subsequent requests  Advantages -The server is able to:  Avoid unnecessary API calls from devices  Gauge the degree of consistency of the template

Implementations “Quickdraw” runs as a background service on Android devices Monitors accelerometer activity Launches pre-configured activities when motion conditions are met

References    machine-learning-applications machine-learning-applications