biometrics and cyber security

© Copyright Daon, 2009 1
Biometrics and Cyber SecurityBiometrics and Cyber Security
Key Considerations in Protecting CriticalKey Considerations in Protecting Critical
InfrastructureInfrastructure –– Now and In The FutureNow 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
1 2 3 4 5
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
1
2
3
4
5
6
7
8
9
10
11
Person
Perspective
System
PerspectiveDefense 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)
FalseNon-MatchRate(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)
FalseNon-MatchRate(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

Thank You – Questions?
Conor White
Email: conor.white@daon.com
Direct: 703 984 4010

biometrics and cyber security

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