2. 1 Figure 2-8: Access Cards Magnetic Stripe Cards Smart Cards  Have a microprocessor and RAM  More sophisticated than mag stripe cards  Release only selected information to different access devices

3. 2 Figure 2-8: Access Cards Tokens  Small device with constantly-changing password  Or device that can plug into USB port or another port RFIDs (Radio-Frequency IDs)  Can be detected and tested without physical contact  Allows easier access; used in Tokyo subways New

4. 3 Figure 2-8: Access Cards Card Cancellation  Requires a central system PINs  Personal Identification Numbers  Short: about 4 digits  Can be short because attempts are manual (10,000 combinations to try with 4 digits)  Should not choose obvious combinations (1111, 1234) or important dates  Provide two-factor authentication

5. 4 Figure 2-9: Biometric Authentication Biometric Authentication  Authentication based on body measurements and motions  Because you always bring your body with you Biometric Systems (Figure 2-10)  Enrollment  Later access attempts  Acceptance or rejection

6. 5 Figure 2-10: Biometric Authentication System 1. Initial Enrollment 2. Subsequent Access User Lee Scanning Applicant Scanning Template Database Brown 10010010 Lee 01101001 Chun 00111011 Hirota 1101110 … 3. Match Index Decision Criterion (Close Enough?) Processing (Key Feature Extraction) A=01, B=101, C=001 User Lee Template (01101001) User Access Data (01111001) Processing (Key Feature Extraction) A=01, B=111, C=001

7. 6 Figure 2-9: Biometric Authentication Verification Versus Identification  Verification: Are applicants who they claim to be? (compare with single template)  Identification: Who is the applicant? (compare with all templates) More difficult than verification  Verification is good for replacing passwords in logins  Identification is good for door access and other situations where entering a name would be difficult

8. 7 Figure 2-9: Biometric Authentication Precision  False acceptance rates (FARs): Percentage of unauthorized people allowed in Person falsely accepted as member of a group Person allowed through a door who should be allowed through it Very bad for security

9. 8 Figure 2-9: Biometric Authentication Precision  False rejection rates (FRRs): Percentage of authorized people rejected Valid person denied door access or server login Can be reduced by allowing multiple access attempts High FRRs will harm user acceptance

10. 9 Figure 2-9: Biometric Authentication Precision  Vendor claims for FARs and FRRs tend to be exaggerated because they often perform tests under ideal circumstances  For instance, having only small numbers of users in the database  For instance, by using perfect lighting, extremely clean readers, and other conditions rarely seen in the real world

11. 10 Figure 2-9: Biometric Authentication User Acceptance is Crucial  Strong user resistance can kill a system  Fingerprint recognition may have a criminal connotation  Some methods are difficult to use, such as Iris recognition, which requires the eye to be lined up carefully. These require a disciplined group

12. 11 Figure 2-9: Biometric Authentication Biometric Methods  Fingerprint recognition Simple, inexpensive, well-proven Weak security: can be defeated fairly easily with copies Useful in modest-security areas  Face recognition Can be put in public places for surreptitious identification (identification without citizen or employee knowledge). More later.

13. 12 Figure 2-9: Biometric Authentication Biometric Methods  Iris recognition Pattern in colored part of eye Very low FARs Somewhat difficult to use: must line up eye exactly or will be rejected High FRR if eye is not lined up correctly can harm acceptance Hand geometry: shape of hand  Voice recognition High error rates Easy to fool with recordings

14. 13 Figure 2-9: Biometric Authentication Biometric Methods  Keystroke recognition Rhythm of typing Normally restricted to passwords Ongoing during session could allow continuous authentication  Signature recognition Pattern and writing dynamics

15. 14 Figure 2-9: Biometric Authentication Biometric Standards  Almost no standardization  Worst for user data (fingerprint feature databases)  Get locked into single vendors

16. 15 Figure 2-9: Biometric Authentication Can Biometrics be Fooled?  Airport face recognition mostly has false positives 4-week trial of face recognition at Palm Beach International Airport Only 250 volunteers in the user database (unrealistically small) Volunteers were scanned 958 times during the trial Only recognized 455 times! Recognition rate fell if wore glasses (especially tinted), looked away Would be worse with larger database Would be worse if photographs were not good

17. 16 Figure 2-9: Biometric Authentication Can Biometrics be Fooled?  DOD Tests indicate poor acceptance rates when subjects were not attempting to evade 270-person test Face recognition recognized person only 51 percent of time Iris recognition only recognized 94 percent of the time. Other research has shown that evasion is often successful for some methods German c’t magazine fooled most face and fingerprint recognition systems Prof. Matsumoto fooled fingerprint scanners 80 percent of the time with a gelatin finger created from a latent (invisible to the naked eye) print on a drinking glass