This document outlines a project focused on evaluating the health tests of entropy sources using Arduino, specifically studying the practicality of the repetition count test and the adaptive proportion test as defined in NIST SP800-90b. The tests aim to detect failures in entropy sources that jeopardize the unpredictability necessary for cryptographic applications. The results indicate successful identification of system failures in simulated environments, providing a basis for future research in improving entropy generation for secure systems.

1. Health Tests of Entropy
Sources on Arduino
By: Jose H. Rodriguez, Jr.
ITL, Computer Security Division
Cryptographic Technology Group
4th August 2016

2. Outline
• Problem
• What the project is about?
• Reasons and explanation of Health Tests
• Test environment
• Results
• Conclusion
• Future work
• Acknowledgements
• Questions?

3. Problem
• Crypto and security applications utilize random numbers and random
bits, but generating them is problematic.
• NIST SP800-90 series
• 90A: Deterministic algorithms
• 90B: Entropy sources
• 90C: Putting whole thing together
• SP800 – 90B describes how to design and test entropy sources

4. ENTROPY SOURCE
Problem
• Need unpredictable crypto keys.
• Unpredictability comes from
entropy source
• What’s an entropy source?
• A ‘black box’ that outputs random
bits that can used for crypto keys.
(User’s perspective)
 Noise Source
• Failure here: (x) security
(x) attackers
• Health Tests counteract this
problem.
• Alerts for entropy loss
Unpredictable
Crypto Keys
System
Failed/entropy
loss

5. What the is project about?
• The aim of this project is to
study the practicality of
different entropy source
health tests in RBGs.
• Focus on health tests of SP800-
90B
• Repetition Count Test &
Adaptive Proportion Test (APT)
• Implemented on Arduino board
– applied to simulated entropy
sources
• Arduino used to imitate a real
world scenario

6. Health Tests • Detect large deviations of the
noise source with high
probability.
• Designed to raise an alarm for
significant decrease in entropy of
outputs:
1. Noise source failures
2. Failure in hardware
3. Faulty implementation
Health Test

7. Health Tests
• Types of health tests:
1. Start up
2. Continuous
3. On demand
• The SP800-90B gives two approved health tests
1. Repetition Count Test
2. Adaptive Proportion Test
• If these tests are utilized, No other tests are required
• Designed for minimal resources and on-the-fly computation

8. Test environment

9. Test environment
Repetition Health Test Adaptive Proportion Test

10. Test environment
• Repetition Count Test (RCT)
• Quickly detect failures
that cause the noise
source to get “stuck” on a
single output value.

11. Test environment
B = 1
C = [1 +
− log2 𝛼
𝐻
] -> 3
A is current sample
B = 4 thus
exceeding C= 3;
so system
failure!

12. Test environment
• Adaptive Proportion Test
(APT)
• Detects a large loss of
entropy; a value becoming
more frequently seen
than expected, given the
source’s assessed entropy
per sample.

13. Test environment
A is current sample value
B = 1
W = 4
C(excel) = CRITBINOM(W, power(2,(-H)), 1 - α) → 2
W = 4
No failure B = 3 exceeds C = 2 so
system failure.

14. Results
• Program memory usage:
• MAX: 32,256 bytes
• Original memory usage:
• Use to be 22% due to:
• Poor program design;
overthinking
• Certain functions; parseInt()
• Slowed down processing speed

15. Results
filename Test H alpha time (hr:min:sec) Failure detected?
Repetition 1 2^-6 0:00:09 yes
APT 1 2^-6 0:13:43 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 - no
Repetition 1 2^-6 0:00:10 yes
APT 1 2^-6 0:05:22 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 - no
Repetition 1 2^-6 0:00:10 yes
APT 1 2^-6 0:05:22 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 - no
Repetition 1 2^-6 0:00:10 yes
APT 1 2^-6 0:05:22 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 - no
Repetition 1 2^-6 0:00:10 yes
APT 1 2^-6 0:13:43 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 0:20:17 yes
Repetition 1 2^-6 0:00:09 yes
APT 1 2^-6 0:13:43 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 0:17:55 yes
Repetition 1 2^-6 0:00:12 yes
APT 1 2^-6 0:13:07 yes
Repetition 1 2^-10 0:00:48 yes
APT 1 2^-10 0:11:56 yes
Repetition 2 2^-6 0:00:03 yes
APT 2 2^-6 - no
Repetition 2 2^-10 0:00:07 yes
APT 2 2^-10 - no
Repetition 2 2^-6 0:00:04 yes
APT 2 2^-6 - no
Repetition 2 2^-10 0:00:07 yes
APT 2 2^-10 - no
Repetition 2 2^-6 0:00:03 yes
APT 2 2^-6 - no
Repetition 2 2^-10 0:00:07 yes
APT 2 2^-10 - no
Repetition 2 2^-6 0:00:04 yes
APT 2 2^-6 - no
Repetition 2 2^-10 0:00:07 yes
APT 2 2^-10 - no
NearUniform -1-stutter-64
NearUniform -1
NearUniform -1-stuck-16
NearUniform -1-stuck-64
NearUniform -1-stuck-128
NearUniform -1-stutter-16
NearUniform -1-stutter-128
NearUniform -2
NearUniform -2-stuck-16
NearUniform -2-stuck-64
NearUniform -2-stuck-128
• Arduino processes data sequences and detects
system failure.
• At a good rate
• Near Uniform
• Entropy levels follow a near uniform dist. with 1
bit of entropy (blue)
• Entropy levels follow a near uniform dist. with 2
bits of entropy (green)
• Stutter: repeats same value n times
• Stuck: repeats the same n symbols from the
failure point on
• H: assessed entropy/sample of the source
• Alpha: false positive probability

16. Conclusions
• The results
• Thoughts on this project
• Thoughts of the SURF Program
• Challenges faced

17. Future Work
The results of this research can be
used as a foundation that sets up
for more future research

18. Acknowledgements
NIST SURF Program
Dr. Tariq Tashtoush - TAMIU
Kerry McKay
John Kelsey

19. Questions?
Thank you!