SoftPUF: a Software-Based Blockchain Framework using PUF and Machine Learning
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 solutions, into the blockchain network. Additionally, the framework incorporates well-established defense mechanisms to ensure robust security against various attacks. This combined approach paves the way for secure and scalable authentication in diverse blockchain-based applications. Additionally, to ensure robust security, the system incorporates well-established defense mechanisms against various attacks, including 51%, phishing, routing, and Sybil attacks, into the blockchain network. This combined approach paves the way for secure and efficient authentication in a wider range of blockchain-based applications.