Hands-On Teaching of Hardware Security for Machine Learning

Hands-On Teaching of Hardware Security for Machine Learning
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机器学习硬件安全实践教学

DOI:
10.1145/3526241.3530828
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发表时间:
2022
期刊:
GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI 2022
影响因子:
--
通讯作者:
Aysu, Aydin
Aysu, Aydin
中科院分区:
--
文献类型:
--
作者:
Calhoun, Ashley;Ortega, Erick;Yaman, Ferhat;Dubey, Anuj;Aysu, Aydin

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机器学习(ML)和人工智能(AI)电路的硬件安全正在成为网络安全框架中的一个主要话题。虽然在这方面正在进行许多研究,但社区忽略了教育部分。在本文中,我们提出了一个培训模块,由一组动手实验,允许教学硬件安全概念的新人。具体来说,我们提出了5个实验和相关的训练材料,这些实验和材料教授神经网络硬件实现上的侧信道攻击和防御。我们报告的组织和测试后,这些实验与大二本科生在北卡罗来纳州州立大学的发现。学生首先学习神经网络的基础知识,然后在实验板上构建神经网络推理电路。然后,他们对硬件进行差分功耗分析攻击以窃取训练的权重,并进行电路平衡(隐藏)式防御以减轻攻击。学生开发所有相关的硬件和软件代码来执行攻击和构建防御。结果表明,数字电路设计,神经网络和侧通道分析等复杂的概念,可以在大二的水平与一组深思熟虑的实验指导。未来的扩展可能包括建立远程教学的在线基础设施,并有效地扩展到更广泛的受众。
Hardware security for machine learning (ML) and artificial intelligence (AI) circuits is becoming a major topic within the cybersecurity framework. Although much research is ongoing on this front, the community omits the educational components. In this paper, we present a training module comprised of a set of hands-on experiments that allow teaching hardware security concepts to newcomers. Specifically, we propose 5 experiments and related training material that teach side-channel attacks and defenses on the hardware implementations of neural networks. We report the organization and the findings after testing these experiments with sophomore undergraduate students at North Carolina State University. The students first study the basics of neural networks and then build a neural network inference circuit on a breadboard. They then conduct a differential power analysis attack on the hardware to steal trained weights and a circuit-balancing (hiding) style defense to mitigate the attack. The students develop all related hardware and software codes to perform attacks and build defenses. The results show that such complex notions of digital circuits design, neural networks, and side-channel analysis can be instructed at the sophomore level with a well-thought set of experiments. Future extensions could include establishing an online infrastructure for remote teaching and efficient scaling to a broader audience.
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影响因子: --
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