WESS 2013

Digital Fingerprints for Low-Cost Platforms using MEMS Sensors

Zane Franklin – Virginia Tech

09/29/2013

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Outline

• Authentication

• Digital Fingerprints

• Sources of Uniqueness

• Implementation Details

• Results

• Conclusions / Future Works

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Introduction

• Authentication is a key component of secure systems

• We use authentication in all secure communications

• In today’s systems, in place of humans, electronic devices communicate over non-secure channels

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A Simple Authentication Protocol

• Prover and Challenger share a secret key

• Prover can verify the possession of a key without showing its content

• We focus on the generation of this Secret Key

Hash Function Hash Function

Authentication

Protocol

Challenger Prover

Secret

Key RNG/CNT

Digest Digest

Secret

Key

?=

Valid/

Fake

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Application: The Internet of Things

• The Internet of Things is a large scale network of electronic devices

• These devices need to securely exchange information

• New challenges for security: How do we ensure the authenticity of these numerous devices?

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Internet of Things = Internet of Sensors

• Low-cost devices utilizing microcontrollers and physical sensors

• Sensors enable devices to monitor environment parameters such as room temperature, navigation speed or ambient noise.

• The Internet of Things that is built with these capabilities would eventually become an Internet of Sensors

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Shifting the Root of Trust

• Traditionally, microcontrollers form the root of trust in a network

• Secret Key must be stored in nonvolatile memory

• Threats:

• Tampering: false negatives

• Cloning: false positives

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• With sensor–based fingerprints, the root of trust is shifted to sensors

• No storage needed

• Multiple sensors can contribute to secret key

Shifting the Root of Trust

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Executive Summary

• We present:

An identification of the sources of uniqueness in a generic MEMS sensor.

Digital fingerprint generation using the uniqueness of a MEMS sensor

A quantification of the quality of generated digital fingerprints and observations of this method’s feasibility.

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Generating the Secret Key

• Biometrics for authentication of individuals are used in systems where humans are the trusted parties.

• Like humans, digital fingerprints are unique to each individual and are hard to clone

• Digital fingerprints inspired by biometrics is an active research area

• How can we find

digital fingerprints?

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Generating the Secret Key – Process Variation

• No two electronic components are identical

• Process variation causes device disparity

• We can exploit the process variation as a device-unique identifier

• A number of digital fingerprinting methods have been presented for SRAMs, FPGAs, microprocessors, etc.

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Sources of Digital Fingerprints

ResourceTechnology

scaleSource of Variation

Measured Variation

Overhead of Operation

SRAM Nanometer ElectricalPower-up Values, Write Collisions

Power cycling, True Dual-Port

SRAMs

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Sources of Digital Fingerprints

ResourceTechnology

scaleSource of Variation

Measured Variation

Overhead of Operation

SRAM Nanometer ElectricalPower-up Values, Write Collisions

Power cycling, True Dual-Port

SRAMs

Microprocessor Nanometer ElectricalExecution

Results of an Instruction

External High-Precision Clock

Generator

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Sources of Digital Fingerprints

ResourceTechnology

scaleSource of Variation

Measured Variation

Overhead of Operation

SRAM Nanometer ElectricalPower-up Values, Write Collisions

Power cycling, True Dual-Port

SRAMs

Microprocessor Nanometer ElectricalExecution

Results of an Instruction

External High-Precision Clock

Generator

Dedicated Logic

Nanometer ElectricalTiming of Delay

Paths

Dedicated Unit, Reconfigurable Fabric (FPGA)

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Sources of Digital Fingerprints

ResourceTechnology

scaleSource of Variation

Measured Variation

Overhead of Operation

SRAM Nanometer ElectricalPower-up Values, Write Collisions

Power cycling, True Dual-Port

SRAMs

Microprocessor Nanometer ElectricalExecution

Results of an Instruction

External High-Precision Clock

Generator

Dedicated Logic

Nanometer ElectricalTiming of Delay

Paths

Dedicated Unit, Reconfigurable Fabric (FPGA)

MEMS Sensors Micrometer MechanicalOffset Values,

Impulse Response

Extra sampling

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MEMS Sensors

• Micro ElectroMechanical Systems

• Accelerometers, gyroscopes, resonators, pressure sensors, etc. all can be implemented in MEMS technology

• Sources of Variation:

• Geometry of structure

• Fabrication dimensions

• Feature complexities

• Scatters of material

• Control voltage

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Target Platform, Experiment Setup

• ADXL345 accelerometer

• PIC32 microcontroller

• Stationary while under test

• 20 sensors

• 100 fingerprints/sensor

• Quality Metrics

• Uniqueness

• Reliability

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Generating the Secret Key – Offset

• Offset values

• Result of process variation in accelerometers

• Position-dependent

• Precision-dependent

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Generating the Secret Key - Impulse

• Electrostatic Impulse

• Creates artificial acceleration

• Implemented as self-test feature

• Impulse is subject to process variation

• Position Independent

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Impulse Response/Self-Test

• Natural acceleration measured

• Impulse enabled, acceleration measured

• Natural acceleration + impulse response

• Difference used for secret key generation

• Position independence: natural acceleration removed

• Impulse variation + offset variation

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Quality Metrics

• Validated on 20 ADXL345 sensors

• Uniqueness is a metric used for estimating how well each device is being authenticated

• Reliability is a metric used for estimating the reproducibility of the generated keys

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Target Platform Effect on Metrics

• Reliability

• Noise increases with output data rate

• Sampling at 100Hz

• Uniqueness

• 48 bit outputs

• 16 bit output/axis

[19]

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Results

• Uniqueness of the offset measurements (left) is 30.2±3.6 (with %90 CI) Reliability is 86.2

• Uniqueness of the impulse + offset measurements (right) is 42.64±4.3 (with %90 CI). Reliability is 92.17

Uniqueness: Offset Values Uniqueness: Offset + Impulse

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Improvements in Future Work

• Outputs of multiple sensors can be combined for a larger key

• Improve uniqueness

• Improve reliability with error correction

• Investigate effects of:

• Temperature variation

• Aging

• Scaling to NEMS

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Conclusions

• MEMS sensors can be successfully used in authentication schemes as unique sources of entropy

• Low-cost, memory-less alternative

• Outputs from multiple sensors in each device can be combined to create more secure secret keys

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Questions

WESS 2013

Digital Fingerprints for Low-Cost Platforms using MEMS Sensors

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Cost of Operation

*Only the sampling cost is shown

Output Data

Rate (Hz)Idd (µa) Power (µW)

Energy

(µJ)

3200 140 252 0.0788

1600 90 162 0.1013*

800 140 252 0.315

400 140 252 0.63

200 140 252 1.26

100 140 252 2.52*

50 90 162 3.24

25 60 108 4.32

12.5 50 90 7.2

6.25 45 81 12.96

3.13 40 72 23.0032

1.56 34 61.2 39.2308

0.78 23 41.4 53.0769

0.39 23 41.4 106.1538

0.2 23 41.4 207

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Previous Work

• Previous work on implementing digital fingerprints on sensors:

• Rosenfald et al. showed the first example.

Photodiode-based sensor designed for fingerprint generation

High reliability and uniqueness

• Boneh et al. used MEMS accelerometer

Use only offset measurements

Requires further statistical quantification

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References

[1] http://beyondplm.com/2012/01/11/plm-smart-products-and-internet-of-things/

[2]http://katv.images.worldnow.com/images/15161314_BG1.jpg

[3]http://www.wired.com/images/slideshow/2008/07/gallery_nano_letters/nano_letters_81_.jpg

[19] Analog Devices.: "ADXL345 datasheet." USA: Analog Devices, 2010