Biometrics and Cyber Security

Biometrics and Cyber Security Key Considerations in Protecting Critical Infrastructure Now and In The Future Conor White, Chief Technology Officer, Daon Copyright Daon, 2009 1

Why is Cyber Security Important in the Context of Biometric Systems? Copyright Daon, 2009 2

Cyber Security & Biometrics On the Internet, nobody knows you are a dog. Or a terrorist Or a student Or a spy Copyright Daon, 2009 3

Identity is More Valuable than Money! I can t think of a single piece of information more critical to our ultimate security and prosperity, both as individuals and as a country, than our personal identity. The ability of an individual to establish identity, to verify you are who you claim to be, is critical to the many transactions that occur in a single day. As the world becomes more interdependent, as transactions become more global, and as the world embraces identity management and assurance as an element of conducting business, personal identities will become a form of global currency. Whether you are crossing a border, seeking employment, applying for a public benefit, opening a bank account, combating crime, making a purchase, enforcing immigration policy, granting access to public and private spaces, detecting terrorists ---- identity verification has limitless value. Governor Tom Ridge Former Secretary, Department of Homeland Security Copyright Daon, 2009 4

Identity Management is Fundamental to Cyber Security Cyber Security is about establishing trust in entities accessing your networks and ensuring that they perform functions consistent with the role you define for them. The fundamental capability necessary for any cyber security solution is Identity Management. Biometrics is a key enabling technology in the fight to strengthen the security of systems against cyber crime However Copyright Daon, 2009 5

Biometric Identity Systems Will Be Attacked! In this session we will discuss (briefly) the following cyber security topics as they relate to Biometric Systems: 1. System Level Perspective 2. Person Level Perspective 3. Independence, Flexibility, Ongoing Analysis and Adaptation Copyright Daon, 2009 6

Attacks on Biometric Systems Biometrics provide a clear benefit to counteracting cyber security threats but biometric systems can themselves be a source of weakness Consider the following: Don t have to duplicate to spoof you just need to alter to ensure no 1:1 or 1:N match (negative identification scenarios) As our databases grow, we struggle to achieve universality As we seek to automate, unattended acquisition and authentication creates risk As more systems are deployed, frequency and sophistication of attacks will increase Microsoft OS virus vs Apple OS virus No Biometric modality is perfect don t believe anybody who tells you otherwise No silver bullet - lots of papers & patents but few commercial offerings Industry starting to look more seriously at liveness detection e.g. LivDet 2009 Copyright Daon, 2009 7

How Do We Compete? Countermeasures are required: Enhanced Capture Software Secured Systems New Capture Devices Multi-factor Multi-modal Supervision & Oversight guiding standard and principles Ability to react through flexible technology and process Biometric matching has been a technology- and tool-centric field. A Defense-in-Depth method of dealing with biometric & identity-related concerns takes a more holistic approach: People Technology Operations Copyright Daon, 2009 8

Biometric System Threats & Countermeasures Copyright Daon, 2009 9

Biometric System Vulnerabilities Person Perspective System Perspective 7 Storage 11 Verifier 6 10 Data Collection Signal Processing Matching Decision 1 2 3 4 5 8 9 Key Considerations: Source: Study report on Biometrics and E-Authentication There is no perfect identity authentication method every form of authentication has vulnerabilities The entire identity eco-system is vulnerable to attack Don t just secure the point of authentication Consider systemic weaknesses as well Must provide a defense-in-depth strategy Copyright Daon, 2009 10

First Principle of Cyber Security Security by Design Security should be designed into a solution and not bolted on after the fact All solutions MUST be designed using industry-best security principles Encryption of data both in transit and at rest Use of strong cryptographic techniques (e.g. HSMs) Robust key management Non-repudiation of events Authorization of function Integrity protection data and system Uses industry proven techniques no security by obscurity Biometrics systems are vulnerable to attack at several points in the process: data collection, signal processing, data storage, and decision/action point Copyright Daon, 2009 11

Defense in Depth Person Perspective System Perspective 7 Storage 11 Verifier 6 10 So How do We Design in the Countermeasures? Data Collection 1 2 Signal Processing 3 4 Matching 5 8 Decision 9 Location Threats Example Countermeasures 1 Data Collection Spoofing Liveness detection - Challenge/response Device substitution 2 Raw Data Replay attack Transmission 3 Signal Processing 5 Matching Manipulation of match (Software) Component replacement scores Hill climbing 7 - Storage Database compromise (reading/replacing template, changing bindings) Multi-modal, policy-based Mutually authenticate device Vendor agnostic architecture Sign data, timestamp, session tokens/nonces, HSM, FIPS Sign components Debugger hostile environment Coarse scoring, trusted sensor, secure channel, limit attempts DB access controls, sign/encrypt templates, store on secure token Audit, digital signature 9 Decision Threshold manipulation Protected function, data protection Copyright Daon, 2009 12

And Don t Forget about Data Security Provide an authentication framework that Securely manages sensitive biometric data. Ensures the privacy of users personal (e.g. biometric) data. Resists attacks launched by insiders/outsiders. Provides for non-repudiation of activities. Integrates with 3rd party applications. Scales to enterprise-wide deployments. Is biometric-agnostic by design. Biometric data must be stored securely Privacy concerns (legislation) Risk of legal challenges to signatures if stolen Assume a hostile network Eavesdropping on sensitive traffic. Injection/deletion of messages Assume a hostile environment Database may be compromised. Machines may be physically attacked. Attacks launched against OS or Daon software. Copyright Daon, 2009 13

In Summary Biometrics enable stronger defense against cyber security attacks but biometric systems need to ensure that they don t become a platform for launching an attack themselves Design Security In Don t just bolt it on Protect biometric systems using a holistic approach Ensure all data is encrypted (in motion and at rest) Ensure robust key management and distribution Signing of all parties in a transaction Tamper evidence and integrity checks throughout system Audit trails and non-repudiation Consider all points in a solution and look for vulnerabilities Its NOT just about the matching algorithm! Copyright Daon, 2009 14

Person-Oriented Attacks & Countermeasures Copyright Daon, 2009 15

Person Oriented Attacks Historically the focus has been finger, face, and iris however, there are several modes being refined: vein, voice, iris on the move,. To defeat a biometric system, sometimes it is sufficient to cause distortion (i.e. to not match). Example, distortion of fingerprints to avoid watchlist hits Universality/Inclusivity becomes a major issue for large populations Multi-Modal solutions work best Systems need an adaptive architecture that can incorporate these new modes and leverage technology improvements over time The most progressive, modern systems begin as a multi-biometric platform with built in systemic security & privacy safeguards and add different biometric capabilities as needed over time! Copyright Daon, 2009 16

Multi-biometric Fusion Use fusion to improve accuracy and robustness Increase accuracy beyond single biometric matching Reduce FTE (broaden population) Spoof/denial resistance Cope with poor quality data Sensor/user fault tolerance Fusion performance depends on: Input data available Comparison algorithm accuracy Correlations between different matcher scores Fusion technique Training data Copyright Daon, 2009 17

Multi-Biometric Fusion in Action Choose a platform that enables multiple biometrics to ensure optimized performance Multi-biometric systems provide key advantages: Increased accuracy (noise reduction) Enhanced Usability Greater Universality Improved Security Improved performance (FMR, FNMR) False Non-Match Rate (FNMR) 0.0001% 0.0010% 0.0100% 0.1000% 1.0000% 10.0000% 100.0000% 100.0000% False Match Rate (FMR) 10.0000% 1.0000% 0.1000% Face Finger Sum fusion Product fusion As enrollment populations grow dramatically, multimodal solutions are inevitable. Performance of large scale identity programs can be significantly improved through the use of multiple biometrics. Large scale systems should establish a core multi-biometric platform first and then choose the most applicable algorithms to suit their population, commercial and performance needs Copyright Daon, 2009 18

In Summary There is NO perfect biometric type There is NO perfect biometric device or algorithm Biometric performance will continue to increase over time, costs will decrease Spoofing attacks will continue and gain in frequency and complexity A flexible framework is needed to counteract these attacks Multi-biometric systems provide best defense with ability to continually add new technology components Policy based normalization and fusion should be kept independent of biometric matching algorithms Adopt a platform that enables you to take advantage of technological improvements over time Copyright Daon, 2009 19

Technology Flexibility, Ongoing Analysis and Adaptation Copyright Daon, 2009 20

Analysis and Adaptation Question: How do you react to: Biometric technologies continuously changing Weaknesses identified in specific algorithms or devices Spoofing techniques continuously improving New normalization and fusion techniques emerging Throughput and performance models emerging Answer: Deploy an analysis and adaptation engine that enables you to do what-if analysis and understand consequences of changes ahead of implementation Identify and correct weak points ahead of cyber attackers Automate performance analysis of what-if scenarios: Algorithms: Matching, Quality, Fusion Devices/sensors Interoperability: Cross-device analysis, multi-algorithm scenarios Protocols e.g. 1:1, 1:N, #attempts, preferred sample types Copyright Daon, 2009 21

Which Fusion? DETs 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+00 False Non-Match Rate (FNMR) 1.0E-01 1.0E-02 517_Face_C 517_Finger_LI SUM: MinMax SUM: Zscore SUM: MAD SUM: TanH PROD: FNMR PROD: Liklihood False Match Rate (FMR) 1.0E-03 Copyright Daon, 2009 22

Self Optimizing Framework for Analysis and Adaptation Policy Based Biometric Platform Biometric Performance Analysis Engine Biometric Performance Results Analysis Analysis Engine Copyright Daon, 2009 23

In Summary Vendor independence provides both a monetary ROI and a cyberthreat risk mitigation Leverage concept of master broker to orchestrate operations of biometric components Ensure a vendor independent framework is put in place Ensure (i.e. prove positively) that your solution is independent of any single biometric technology provider Maintain strict data independence from underlying device or matcher technology Large scale programs can clearly benefit for performance analysis tools to ensure optimum use of biometrics Deploying a system that leverages synergies between an identification broker and analysis tools enables systems to be self optimizing over time yielding better performance and mitigating against cyber security threats Copyright Daon, 2009 24

Thank You Questions? Conor White Email: conor.white@daon.com Direct: 703 984 4010 Copyright Daon, 2009 25