Abstract
Physical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can be effectively predicted using different machine learning algorithms. In this work, Artificial Neural Networks (ANNs) using swarm intelligence-based modeling attacks are used against different silicon-based PUFs to test their resiliency to these attacks. Amongst the swarm intelligence algorithms, the Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Particle Swarm Optimizer (PSO) and the Grey Wolf Optimizer (GWO) are used. The attacks are extensively performed on six different types of PUFs; namely, Configurable Ring Oscillator, Inverter Ring Oscillator, XOR-Inverter Ring Oscillator, Arbiter, Modified XOR-Inverter Ring Oscillator, and Hybrid Delay Based PUF. From the results, it can be concluded that the first four PUFs under study are vulnerable to ANN swarm intelligence-based models, and their responses can be predicted with an average accuracy of 71.1% to 88.3% for the different models. However, for the Hybrid Delay Based PUF and the Modified XOR-Inverter Ring Oscillator PUF, which are especially designed to thwart machine learning attacks, the prediction accuracy is much lower and in the range of 9.8% to 14.5%.
Highlights
In recent years, the use of programmable devices such asField Programmable Gate Arrays (FPGAs) and custom designed Application Specific Integrated Circuits (ASICs) have increased rapidly
EXPERIMENTAL RESULTS ANALYSIS AND DISCUSSIONS To analyze the vulnerability of the various physical unclonable functions (PUFs) to ANNbased attacks using Swarm Intelligence algorithms, a subset of the randomly chosen Challenge-Response Pairs (CRPs) is used as the training set
Various Machine Learning based attack models have been used recently to breach the security of PUFs
Summary
The use of programmable devices such asField Programmable Gate Arrays (FPGAs) and custom designed Application Specific Integrated Circuits (ASICs) have increased rapidly. Other attempts include implanting malicious electronic circuitry in the chips, known as Trojans, to steal vital information for cyber-attacks These tampered chips can subsequently act as ‘spy chips’ by collecting confidential data for adversaries and hackers. The significant advantage of using PUFs as security measures is that it does not require on-chip memory to generate and store keys; it eliminates the use of on-chip memory for the security of the hardware-based system. Another very significant feature of the PUF is that the keys generated by the PUF are device specific. It should be noted that the behavior of PUFs rely on the random
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