User Authentication Based on Representative Users

Saddamhusain Hadimani

01FM15ECS030

1

Authentication

2

Types Of Authentication

3

Behavioral Biometric Authentication System

4

Keystroke Biometrics A keystroke dynamic is based on the assumption that

each person has a unique keystroke rhythm. KBBASs can be distinguished according to training data

they use: 1.Static(Fixed) Text 2.NonStatic(Free) Text

Biometric classification accuracy measures1) FRR - False Rejection Rate (ii)2) FAR - False Acceptance Rate (iii)3) EER -Equal Error Rate FRR = FAR4) TPR-True Positive Rate (i)5) AUC-Area Under The Curve FAR=TPR

5

Proposed Algorithms

Inner-Cluster Nearest-Neighbor Approach

Structure Of feature

6

Methodology Flowchart

7

Some Opportunities: Login information

Computer Cell phonesAutomated Teller Machine Digital electronic security keypad at a

building entrance Continuous authentication

Online examination

8

Advantages of keystroke dynamics Software Only method. (No Additional

Hardware except a Keyboard) Simple To Deploy and Use (username

& passwords) – Universally accepted Cost Effective No End-User Training It provides a simple natural way for

increased computer security Can be used over the internet

9

Keystroke drawbacks: User’s susceptibility to fatigue Dynamic change in typing patterns Injury, skill of the user Change of keyboard hardware.

10

Keystroke Challenges Which methods have lower error rate? Error rate comparison is difficult Work with very short sample texts Requires adaptive learning

11

Conclusions Combined features of maximum pressure

with latency effective way to verify authorized user

It seems promising , still needs more efforts specially for identificationIris scanners provide the lowest total error rate -

on the order of 10-6 in many cases Even fingerprints provide an error rate on the

order of 10-2

12

Future work Using longer fixed texts Combining many features

increase the accuracy of keystroke analysis Find the most efficient features Adding mouse dynamic

Helpful for identification Including Special characters Future research to reduce FAR & FRR

13

References Alon Schclar, Lior Rokach, Adi Abramson, and Yuval Elovici,” User Authentication

Based on Representative Users”, IEEE Transactions On Systems, Man, And Cybernetics—part C: Applications And Reviews, Vol. 42, No. 6, November 2012

J. Bechtel, “Passphrase authentication based on typing style through an ART 2 Neural network,” IJCIA Vol. 2, No. 2 (2002) pp 1 –22.

A. Peacock, “Typing Patters: A Key to User Identification,” IEEE Security and Privacy, September / October 2004, pp 40- 47.

L. Araujo, “User Authentication Through Typing Biometrics Features,” IEEE Transactions on Signal Processing, Vol. 53, No. 2, February 2005.

A. Guven, “Understanding users’ keystroke patters for computer access security,” Computers & Security, Vol. 22, No. 8, 2003, pp 695-706.

F. Monrose “Keystroke dynamics as a biometric for authentication,” Future Generation Computer Systems, Vol. 16, 2000, pp. 351-359.

M. Obiadat, “An On-Line Neural Network System for Computer Access Security,” IEEE Transactions On Industrial Electronics, Vol. 40, No. 2, April 1993, pp. 235-242.

14

THANK YOUTHANK YOU

15