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Enas Abulibdeh

and 5 more

Physically unclonable functions (PUFs) are circuit primitives that offer a promising and cost-effective solution for various security applications, such as integrated circuits (IC) counterfeiting, secret key generation, and lightweight authentication. PUFs leverage semiconducting variations of ICs to extract intrinsic responses based on applied challenges, establishing unique challenge-response pairs (CRPs) for each device. The security analysis of PUFs is crucial to identify the device weaknesses and ensure response integrity. Accordingly, CRP-based examination plays a major role in defining the resistivity of the block against general and modeling-based attacks. Such analysis requires an updated and representative dataset for training and evaluation. However, there is a lack of benchmark datasets for assessing the effectiveness and resistance of PUF devices. Motivated by this, in this work, we generate a dataset of 300K CRPs for a digital-based PUF implemented on a field programmable gate array (FPGA). The dataset provides a significant number of CRPs for a multi-bit response, where the spatial and temporal adjacency are implicitly defined in the extracted CRPs. Moreover, we investigate different approaches utilizing the generated dataset such as machine learning-based modeling, correlation analysis, and entropy analysis. The CRPs are employed to train linear and nonlinear Support Vector Machine (SVM) models, and the prediction accuracy of SVM models is used as an indicator of the PUF's vulnerability to modeling attacks. As the prediction accuracy does not exceed 65% over 10K CRPs, the extracted dataset sufficiently verifies the resiliency of the device against ML-based modeling attacks. Additionally, Pearson's coefficient is computed on a 10 K-bit vector to determine the correlation between the bits of the response. The calculations expose some correlations between ±0.25, which warns from potential threads. Finally, the paper discusses some potential future research directions and challenges that are envisioned to enhance the security performance of PUFs.

Tasneem Assaf

and 8 more

Physical layer security (PLS) can be adopted for efficient key sharing in secured wireless systems. The random nature of the wireless channel and channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable high-rate secret key generation (SKG) process. The proposed system integrates physically unclonable functions (PUFs) and CR principle to securely exchange secret keys between two nodes. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. The proposed system can operate efficiently even when the channel is nearly flat or time invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. We also propose a novel bit extraction scheme that reduces the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs), which is substantially faster than other PLS techniques. Moreover, the results show that inducing AF into static channels reduces the mismatch ratio between the generated secret sequences and degrades the eavesdropper’s capability to predict the secret keys.