Amy C. Foster

OPTICS
h-index17
3papers
20citations
Novelty63%
AI Score28

3 Papers

OPTICSFeb 5, 2024
Machine Learning Resistant Amorphous Silicon Physically Unclonable Functions (PUFs)

Velat Kilic, Neil Macfarlane, Jasper Stround et al.

We investigate usage of nonlinear wave chaotic amorphous silicon (a-Si) cavities as physically unclonable functions (PUF). Machine learning attacks on integrated electronic PUFs have been demonstrated to be very effective at modeling PUF behavior. Such attacks on integrated a-Si photonic PUFs are investigated through application of algorithms including linear regression, k-nearest neighbor, decision tree ensembles (random forests and gradient boosted trees), and deep neural networks (DNNs). We found that DNNs performed the best among all the algorithms studied but still failed to completely break the a-Si PUF security which we quantify through a private information metric. Furthermore, machine learning resistance of a-Si PUFs were found to be directly related to the strength of their nonlinear response.

OPTICSNov 6, 2017
Information-Dense Nonlinear Photonic Physical Unclonable Function

Brian C. Grubel, Bryan T. Bosworth, Michael R. Kossey et al.

We present a comprehensive investigation into the complexity of a new private key storage apparatus: a novel silicon photonic physical unclonable function (PUF) based on ultrafast nonlinear optical interactions in a chaotic silicon microcavity that is both unclonable and impossible to emulate. This device provides remarkable improvements to total information content (raw cryptographic material), information density, and key generation rates over existing optical scattering PUFs and is also more easily integrated with both CMOS electronics and telecommunications hardware. Our device exploits the natural nonlinear optical behavior of silicon to neutralize commonly used attacks against PUFs and vastly enhance device complexity. We confirm this phenomenon with thorough experimental results on prototype devices and present a detailed estimate of their total information content. Our compact, micron-scale approach represents an entirely new generation of ultrafast and high information density photonic PUF devices that can be directly incorporated into integrated circuits to ensure authenticity and provide secure physical storage of private key material.

CRNov 4, 2017
Secure Communications using Nonlinear Silicon Photonic Keys

Brian C. Grubel, Bryan T. Bosworth, Michael R. Kossey et al.

We present a secure communication system constructed using pairs of nonlinear photonic physical unclonable functions (PUFs) that harness physical chaos in integrated silicon micro-cavities. Compared to a large, electronically stored one-time pad, our method provisions large amounts of information within the intrinsically complex nanostructure of the micro-cavities. By probing a micro-cavity with a rapid sequence of spectrally-encoded ultrafast optical pulses and measuring the lightwave responses, we experimentally demonstrate the ability to extract 2.4 Gb of key material from a single micro-cavity device. Subsequently, in a secure communications experiment with pairs of devices, we achieve bit error rates below $10^{-5}$ at code rates of up to 0.1. The PUFs' responses are never transmitted over the channel or stored in digital memory, thus enhancing security of the system. Additionally, the micro-cavity PUFs are extremely small, inexpensive, robust, and fully compatible with telecommunications infrastructure, components, and electronic fabrication. This approach can serve one-time pad or public key exchange applications where high security is required