"Side-channel Analysis Supports in Physical Unclonable Function Modelling"

Physical Unclonable Function (PUF) relies on its unclonable variation in manufacturing process to generate a unique unclonable digital fingerprint and is used to identify the connected devices. Even though it's work relies on unpredictable variations and working environment, some research shown that its operations are model-able. This project aims to apply machine learning based side-channel analysis (SCA) to build a model for strong PUF, and so achieves high accuracy for challenge-response pair prediction for strong PUF in the context of working environment.

PUF relies on its unclonable variation in manufacturing process to generate a unique response to a challenge. Its unique response can be used to identify and authorise the device in a connected system. However, the unclonable manufacturing variation of the most popular types of silicon PUF, Arbiter PUF (APUF) design is model-able but changed with the working environment like temperature, power supply and ages.

In one hand, the model-ability feature creates a hardware security threat to connected systems like smart health care or automobile when attacker can model the PUF operation. In other hand, the instability in manufacturing variation and working environment makes difficulty to modelling efforts but also reduces the ability of PUF in identifying and authorizing a device in the systems.

SCA, especially the machine learning based SCA would help building high accuracy PUF model. This project will

- Evaluate the vulnerability of strong PUF (like arbiter PUF) under SCA and show requirement for PUF protection under SCA.

- Apply machine learning based SCA in modelling various strong PUF implementations used for devices identification and authorization in connected systems.