SoftPUF: a Software-Based Blockchain Framework using PUF and Machine Learning
S M Mostaq Hossain, Sheikh Ghafoor, Kumar Yelamarthi, Venkata Prasanth Yanambaka

TL;DR
This paper introduces SoftPUF, a software-based PUF mimicking approach combined with machine learning, enabling secure, hardware-independent device authentication within blockchain networks for diverse applications.
Contribution
It presents a novel software-based PUF framework using machine learning to generate device-specific keys, broadening blockchain authentication beyond hardware-dependent solutions.
Findings
Enables secure device authentication without specialized hardware.
Integrates defense mechanisms against common blockchain attacks.
Facilitates legacy device inclusion in blockchain networks.
Abstract
Physically Unclonable Function (PUF) offers a secure and lightweight alternative to traditional cryptography for authentication due to their unique device fingerprint. However, their dependence on specialized hardware hinders their adoption in diverse applications. This paper proposes a novel blockchain framework that leverages SoftPUF, a software-based approach mimicking PUF. SoftPUF addresses the hardware limitations of traditional PUF, enabling secure and efficient authentication for a broader range of devices within a blockchain network. The framework utilizes a machine learning model trained on PUF data to generate unique, software-based keys for each device. These keys serve as secure identifiers for authentication on the blockchain, eliminating the need for dedicated hardware. This approach facilitates the integration of legacy devices from various domains, including cloud-based…
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Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Digital Media Forensic Detection · Chaos-based Image/Signal Encryption
