# Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices

arXiv: https://arxiv.org/abs/2409.17902 · HTML: https://mlsanity.com/paper/2409.17902/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 85 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [Resource Management in Fog/Edge Computing: A Survey](https://mlsanity.com/paper/1810.00305/) (2018)
- [MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants](https://mlsanity.com/paper/2207.09744/) (2022)
- [A New Security Boundary of Component Differentially Challenged XOR PUFs Against Machine Learning Modeling Attacks](https://mlsanity.com/paper/2206.01314/) (2022)
- [Attacking Delay-based PUFs with Minimal Adversary Model](https://mlsanity.com/paper/2403.00464/) (2024)
- [A novel reliability attack of Physical Unclonable Functions](https://mlsanity.com/paper/2405.13147/) (2024)

## Abstract (arXiv)

The rapid expansion of Internet of Things (IoT) devices demands robust and resource-efficient security solutions. Physically Unclonable Functions (PUFs), which generate unique cryptographic keys from inherent hardware variations, offer a promising approach. However, traditional PUFs like Arbiter PUFs (APUFs) and XOR Arbiter PUFs (XOR-PUFs) are susceptible to machine learning (ML) and reliability-based attacks. In this study, we investigate Component-Differentially Challenged XOR-PUFs (CDC-XPUFs), a less explored variant, to address these vulnerabilities. We propose an optimized CDC-XPUF design that incorporates a pre-selection strategy to enhance reliability and introduces a novel lightweight architecture to reduce hardware overhead. Rigorous testing demonstrates that our design significantly lowers resource consumption, maintains strong resistance to ML attacks, and improves reliability, effectively mitigating reliability-based attacks. These results highlight the potential of CDC-XPUFs as a secure and efficient candidate for widespread deployment in resource-constrained IoT systems.