DARPA-BAA-12-06 Proposal 2012

Active Authentication

Technical POC: Dr. Charles Tappert

Principal Investigators: Drs. Tappert, Cha, Chen, Grossman

DARPA – Active Authentication

DARPA Proposal 2012

Request for Proposal (RFP) https://www.fbo.gov/index?s=opportunity&mode=for

m&id=c7968647352f0276fc1b28817c581d86&tab=core&_cview=0

Proposal Due March 6, 2012 Work Duration: 4/1/12 - 3/31/13

Continuations possible Perks – we are applying for

3-4 DPS half tuitions (start with half tuitions per semester)

2 Masters half tuitions

DARPA – Active Authentication

Active Authentication

Gov’t wants to continually authenticate all users of gov’t desktop computers

Objective – detect intruders (unauthorized users) of gov’t machines Continual authentication – ongoing but

with possible interruptions in contrast to continuous authentication which

would mean without interruption

DARPA – Active Authentication

Proposed Technologies

Keystroke/Mouse Biometrics Unconscious level: ballistic motor

control Stylometry

Linguistic level: character, word, syntax

Intentional Behavior Semantic level

DARPA – Active Authentication

DPS Keystroke Biometric Dissertations

1. Long-text keystroke biometric applications over the Internet Dr. Mary Curtin, 2006

2. Keystroke biometric recognition studies on long text input Dr. Mary Villani, 2006

3. Strategies for managing missing and incomplete information Dr. Mark Ritzmann, 2007

4. An improved kNN classifier with appl to keystroke bio authentication Dr. Robert Zack, 2010

5. An investigation of keystroke and stylometry traits John Stewart (in progress, almost done - manuscript being reviewed)

6. Keystroke biometric intrusion detection Ned Bakelman (in progress)

DARPA – Active Authentication

Possible DPS Dissertation Problems

(in order of recommended priority)

1. New or improved biometric systems Keystroke/mouse biometric system (motor control level) Stylometry biometric system (char/word/syntax linguistic level) Intruder operational behavior biometric system (semantic level)

2. Dichotomy model – weak versus strong training For large populations we can’t train system on all users

3. Continual training of the biometric systems Continual training – how often?, measure performance Explore methods – e.g., give more weight to recent training data

4. Weak biometrics (mouse, stylometry) assist strong ones (keystroke) Clustering method suggested

5. Evaluate setting ROC-curve operating point for individual users to explicitly control individual risk mitigation