Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems

Area of Science:

• Biometrics
• Cybersecurity
• Embedded Systems

Background:

• Digital fingerprint biometrics are increasingly used for access control.
• Secure elements are employed in biometric systems for template storage and on-card comparison.
• Understanding vulnerabilities is crucial for enhancing security and privacy.

Purpose of the Study:

• To analyze how a priori information can be exploited in fingerprint-based biometric attacks.
• To develop and propose a novel countermeasure against brute force and zero effort attacks.
• To demonstrate the impact of fingerprint classification on attack and defense strategies in embedded systems.

Main Methods:

• Detailed analysis of a priori information (e.g., classification, sensor type, resolution, minutiae count) for potential exploitation.
• Development of a new countermeasure leveraging fingerprint classification for minutiae templates.
• Experimental validation on significant fingerprint datasets.

Main Results:

• Demonstrated a novel attack vector exploiting a priori fingerprint information.
• Proposed an effective countermeasure against brute force and zero effort attacks.
• Experimental results highlight the significant impact of fingerprint classification on system security.

Conclusions:

• Fingerprint classification plays a critical role in both the vulnerability and defense of embedded biometric systems.
• The proposed countermeasure offers enhanced security against specific attack types.
• Further research into classification-aware security mechanisms is warranted.