轻量级PUF认证网络，用神经网络+分治学习防破解
LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
- 串联神经网络+分治学习，降低计算负担
- 支持双向认证，可扩展至超75亿设备
- 抗机器学习攻击，适合6G物联网安全
到2025年，全球物联网（IoT）设备预计超过750亿台，深刻改变城乡环境的交互方式。然而，物联网设备的安全性，尤其是认证环节仍面临挑战，传统密码学方法受限于设备的算力与存储能力。本文将物理不可克隆函数（PUF）作为高安全性解决方案，利用其固有的物理特性实现设备认证。但传统PUF系统易受机器学习攻击，且依赖大规模数据集。为此，本文提出一种轻量级PUF机制LPUF-AuthNet，结合串联神经网络（TNN）与分治学习（SL）范式。该方法具备可扩展性，支持双向认证，并通过抵抗多种攻击提升安全性，为未来6G技术中的安全集成铺平道路。
原文摘要 · Abstract (English)
By 2025, the internet of things (IoT) is projected to connect over 75 billion devices globally, fundamentally altering how we interact with our environments in both urban and rural settings. However, IoT device security remains challenging, particularly in the authentication process. Traditional cryptographic methods often struggle with the constraints of IoT devices, such as limited computational power and storage. This paper considers physical unclonable functions (PUFs) as robust security solutions, utilizing their inherent physical uniqueness to authenticate devices securely. However, traditional PUF systems are vulnerable to machine learning (ML) attacks and burdened by large datasets. Our proposed solution introduces a lightweight PUF mechanism, called LPUF-AuthNet, combining tandem neural networks (TNN) with a split learning (SL) paradigm. The proposed approach provides scalability, supports mutual authentication, and enhances security by resisting various types of attacks, paving the way for secure integration into future 6G technologies.
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