TY - JOUR
T1 - PolyPUF: Physically Secure Self-Divergence
AU - Konigsmark, Sven Tenzing Choden
AU - Chen, Deming
AU - Wong, Martin D. F.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7
Y1 - 2016/7
N2 - Physically unclonable functions (PUFs) provide a device-unique challenge-response mapping and are employed for authentication and encryption purposes. Unpredictability and reliability are the core requirements of PUFs: unpredictability implies that an adversary cannot sufficiently predict future responses from previous observations. Reliability is important as it increases the reproducibility of PUF responses and hence allows validation of expected responses. However, advanced machine-learning algorithms have been shown to be a significant threat to the practical validity of PUFs, as they are able to accurately model PUF behavior. The most effective technique was shown to be the XOR-based combination of multiple PUFs, but as this approach drastically reduces reliability, it does not scale well against software-based machine-learning attacks. In this paper, we analyze threats to PUF security and propose PolyPUF, a scalable and secure architecture to introduce polymorphic PUF behavior. This architecture significantly increases model-building resistivity while maintaining reliability. An extensive experimental evaluation and comparison demonstrate that the PolyPUF architecture can secure various PUF configurations and is the only evaluated approach to withstand highly complex neural network machine-learning attacks. Furthermore, we show that PolyPUF consumes less energy and has less implementation overhead in comparison to lightweight reference architectures.
AB - Physically unclonable functions (PUFs) provide a device-unique challenge-response mapping and are employed for authentication and encryption purposes. Unpredictability and reliability are the core requirements of PUFs: unpredictability implies that an adversary cannot sufficiently predict future responses from previous observations. Reliability is important as it increases the reproducibility of PUF responses and hence allows validation of expected responses. However, advanced machine-learning algorithms have been shown to be a significant threat to the practical validity of PUFs, as they are able to accurately model PUF behavior. The most effective technique was shown to be the XOR-based combination of multiple PUFs, but as this approach drastically reduces reliability, it does not scale well against software-based machine-learning attacks. In this paper, we analyze threats to PUF security and propose PolyPUF, a scalable and secure architecture to introduce polymorphic PUF behavior. This architecture significantly increases model-building resistivity while maintaining reliability. An extensive experimental evaluation and comparison demonstrate that the PolyPUF architecture can secure various PUF configurations and is the only evaluated approach to withstand highly complex neural network machine-learning attacks. Furthermore, we show that PolyPUF consumes less energy and has less implementation overhead in comparison to lightweight reference architectures.
KW - Information security
KW - Machine learning
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84976479103&origin=inward
U2 - 10.1109/TCAD.2015.2488493
DO - 10.1109/TCAD.2015.2488493
M3 - Journal article
SN - 0278-0070
VL - 35
SP - 1053
EP - 1066
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 7
ER -