Biometrics and Cyber Security Biometrics and Cyber Security
Key Considerations in Protecting Critical Key Considerations in Protecting Critical
Infrastructure
Infrastructure – – Now and In The Future Now and In The Future
Conor White, Chief Technology Officer, Daon
Why is Cyber Security
Important in the Context of
Biometric Systems?
Cyber Security & Biometrics
On the Internet, nobody knows you are a dog….
Or a terrorist …Or a student … Or a spy…
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
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…
…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
Attacks on Biometric Systems
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
Biometrics provide a clear benefit to
counteracting cyber security threats – but
biometric systems can themselves be a
source of weakness
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
Biometric System Threats &
Countermeasures
Biometric System Vulnerabilities
Key Considerations:
• 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
Signal Processing Data
Collection
Matching Storage
Decision Verifier
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6 7
8 9
10 11
Person
Perspective
System
Perspective
Source: Study report on Biometrics and E-Authentication
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
Location Threats Example Countermeasures
1 – Data Collection Spoofing Liveness detection - Challenge/response Multi-modal, policy-based
Device substitution Mutually authenticate device Vendor agnostic architecture
2 – Raw Data Transmission
Replay attack Sign data, timestamp, session tokens/nonces, HSM, FIPS
3 – Signal Processing
(Software) Component replacement
Sign components
5 – Matching Manipulation of match scores
Debugger hostile environment
Hill climbing Coarse scoring, trusted sensor, secure channel, limit attempts
7 - Storage Database compromise (reading/replacing template, changing bindings)
DB access controls, sign/encrypt templates, store on secure token
Audit, digital signature
9 – Decision Threshold manipulation Protected function, data protection
Signal Processing Data
Collection
Matching Storage
Decision Verifier
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6 7
8 9
10 11
Person Perspective
System Perspective
Defense in Depth
So How do We Design in
the Countermeasures?
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.
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!
Person-Oriented Attacks &
Countermeasures
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!
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
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)
As enrollment populations grow dramatically, multi- modal 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
0.1000%
1.0000%
10.0000%
100.0000%
0.0001% 0.0010% 0.0100% 0.1000% 1.0000% 10.0000% 100.0000%
False Match Rate (FMR)
False Non-Match Rate (FNMR)
Face Finger Sum fusion Product fusion
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
Technology Flexibility, Ongoing Analysis and
Adaptation
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
Which Fusion? DETs
1.0E-03 1.0E-02 1.0E-01 1.0E+00
1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00
False Match Rate (FMR)
False Non-Match Rate (FNMR)
517_Face_C 517_Finger_LI SUM: MinMax SUM: Zscore SUM: MAD SUM: TanH PROD: FNMR PROD: Liklihood
Self Optimizing Framework for Analysis and Adaptation
Biometric Performance
Analysis Engine
Results Analysis Policy Based
Biometric Platform
Biometric Performance
Analysis Engine
In Summary
Vendor independence provides both a monetary ROI and a cyber- threat 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
