Chapter 12 Thwarting Attacks

1. Chapter 12Thwarting Attacks Leandro A. Loss

2. Introduction • Benefits of Biometric Authentication: • Convenience (e.g. recall password, keep cards) • Security (e.g. cracked password, stolen cards) • Introduces different security weaknesses: • Objective: Identify security weak points, keeping in mind the security versus convenience trade-off

3. Pattern Recognition Model Sensor Template Extractor Matcher Application Enrollment Template Database • 11 basic points of attack that plague biometric authentication systems

4. Attacking Biometric Identifiers Sensor Template Extractor Matcher Application

5. Attacking Biometric Identifiers • Coercive Attack Examples • A genuine user is forced by an attacker to identify him or herself to an authentication system; • The system should detect coercion instances reliably without endangering lives (stress analysis, guards, video recording). • The correct biometric is presented after physical removal from the rightful owner; • The system should detect “liveness” (movements of iris, electrical activity, temperature, pulse in fingers.

6. Attacking Biometric Identifiers • Impersonation Attack Examples • Involves changing one’s appearance so that the measured biometric matches an authorized person; • Voice and face are the most easily attacked; • Fake fingerprints or even fingers have been reported. • Changes one’s appearance to cause a false negative error in screening systems; • disguises or plastic surgeries; • Combination of multiple biometrics makes replications more difficult, specially when synchronization is analyzed (works well for the first case); • No defense suggestions for the second case;

7. Attacking Biometric Identifiers • Replay Attack Examples • Re-presentation of previously recorded biometric information (tape or picture); • Prompt random text to be read; • Detect tri-dimensionality or require change of expression.

8. Front-end attacks B D Sensor Template Extractor Matcher Application A C

9. Front-end attacks • (A) Channel between sensor and biometric system • Replay Attacks: • circumventing the sensor by injecting recorded signal in the system input (easier than attacking the sensor); • digital encryption and time-stamping can protect against these attacks. • Electronic Impersonation Attacks: • Injection of an image created artificially from extracted features; • e.g. An image of an artificial fingerprint created from minutia captured from a card; • No defense suggested.

10. Front-end attacks • (B) Template Extractor • Trojan Horse Attacks: • The features are replaced after extracted (assuming the representation is known); • The extractor would produce a pre-selected feature set at some given time or under some condition; • No defense suggested.

11. Front-end attacks • (C) Transmissions between Extractor and Matcher • Communication Attacks: • Specially dangerous in remote matchers; • No defense suggested.

12. Front-end attacks • (D) Matcher • Trojan Horse Attacks: • Manipulations of match decision; • e.g. A hacker could replace the biometric library on a computer with a library that always declares a true match for a particular person; • No defense suggested.

13. Circumvention Sensor Template Extractor Matcher Application “Overriding of the matcher’s output”

14. Circumvention • Collusion • Some operators have super-user status, which allows them to bypass the authentication process; • Attackers can gain super-user status by: • - Stealing this status; • - Agreement with operator;

15. Circumvention • Covert Acquisition • Biometric stolen without the user knowledge; • Only the parametric data is used to override matcher (so different from impersonation);

16. Circumvention • Denial • A authentic user identifies him or herself to the system but is denied such an access (a False Rejection is evoked); • Not considered fraud because no unauthorized access was granted; • But it disrupts the functioning of the system.

17. Back-end attacks D Sensor Template Extractor Matcher Application B A Enrollment Template Database E C

18. Back-end attacks • (A) Enrollment Attacks • Same vulnerable points of the others; • With collusion between the hacker and the supervisor of the enrollment center, it is easy to enroll a created or stolenidentity; • Enrollment needs to be more secure than authentication and is best done under trusted and competent supervision. Enrollment Template Database Sensor Template Extractor Matcher

19. Back-end attacks • (B) Transmissions between Matcher and Database • Communication Attacks: • Remote central or distributed databases; • Information is attacked before it reaches the matcher.

20. Back-end attacks • (C) Transmissions between Enrollment and Database • Communication Attacks: • Remote central or distributed databases; • Information is attacked before it reaches the database.

21. Back-end attacks (D) Attacks to the Application

22. Back-end attacks • (E) Attacks to the Database • Hacker’s Attack • Modification or deletion of registers: • Legitimate unauthorized person; • Denial of authorized person; • Removal of a known “wanted” person from screening list. • Privacy Attacks: • Access to confidential information; • Level of security of different systems; • Passwords x Biometrics.

23. Other attacks • Password systems are vulnerable to brute force attacks; • The number of characters is proportional to the bit-strength of password; • Biometrics: equivalent notion of bit-strength, called intrinsic error rate (chapter 14);

24. Other attacks • Hill Climbing: • Repeatedly submit biometric data to an algorithm with slight differences, and preserve modifications that result in an improved score; • Can be prevented by • Limiting the number of trials; • Giving out only yes/no matches.

25. Other attacks • Swamping: • Similar to brute force attack, exploiting weakness in the algorithm to obtain a match for incorrect data. • E.g. Fingerprints: • Submit a print with hundreds of minutiae in the hope that at least the threshold number of them will match the stored template; • Can be prevented by normalizing the number of minutiae.

26. Other attacks • Piggy-back: • An unauthorized user gains access through simultaneous entry with a legitimate user (coercion, tailgating).

27. Other attacks • illegitimate enrollment: • Somehow an attacker is enrolled (collusion, forgery).

28. Combining Smartcards and Biometrics • Biometrics – reliable authentication; • Smartcards – store biometrics and other data; • Suggestion: valid enrolled biometrics + valid card; • Benefits: • Authentication is done locally – cuts down on communication with database; • The information never leaves the card – secure by design; • Attacks occur locally and are treated locally; • Keeps privacy;

29. Challenge-Response Protocol • Dynamic authentication - prevents mainly Replay Attacks; • The system issues a challenge to the user, who must respond appropriately (prompted text – increases the difficulty of recorded biometrics’ use); • It will demand more sophisticated attacks and block the casual ones; • Extension: • E.g. Number projected in the retina, that must be typed.

30. Cancellable Biometrics • Once a biometric identifier is somehow compromised, the identifier is compromised forever; • Privacy: • A hacked system can give out user’s information (medical history and susceptibility); • Proscription: • Biometric information should not be used for any other purpose than its intended use; • Concerns • Not an extra bit of information should be collected; • Data integrity and data confidentially are two important issues; • Cross-matching: matching against law enforcement databases; • Biometric cannot change (issue a new credit card number, etc).

31. Cancellable Biometrics • Cancellable biometrics is a technique that alleviate some of these concerns. • Biometrics are distorted by some non-invertible transform. • If one representation is compromised, another one can be generated. • Signal domain distortions: • Distortion of the raw biometric signal: • Morphed fingerprint; • Split voice signal and scramble pieces; • Feature domain distortions: • Distortion of preprocessed biometric signal (template): • Fingerprint minutiae (S={(xi, yi, θi); i=1,…,M}); X1 X2 x1 x2 x3 X3

32. Cancellable Biometrics • Relation to compression and encryption • Signal Compression: • the signal temporarily loses its characteristics; • Encryption: • Secure transmission: signal is restored after it; • Cancellable Biometrics: • Signal loses definitely its characteristics; • It’s desirable that the distorted signal is impossible to be restored.

33. Questions?