.png){kind=icon}
返回
Neural Network Physically Unclonable Function: A Trainable Physically Unclonable Function System with Unassailability against Deep Learning Attacks Using Memristor Array
.png){kind=icon}
OA
AI
DOI：10.1002/aisy.202100111.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The dissemination of edge devices drives new requirements for security primitives for privacy protection and chip authentication. Memristors are promising entropy sources for realizing hardware-based security primitives due to their intrinsic randomness and stochastic properties. With the adoption of memristors among several technologies that meet essential requirements, the neural network physically unclonable function (NNPUF) is proposed, a novel PUF design that takes advantage of deep learning algorithms. The proposed design integrated with the memristor array can be constructed easily because the system does not depend on write operation accuracy. To contemplate a nondifferentiable module during training, an original concept of loss called PUF loss is devised. Iterations of weight update with the loss function bring about optimal NNPUF performance. It is shown that the design achieves a near-ideal 50% average value for security metrics, including uniformity, diffuseness, and uniqueness. This means that the NNPUF satisfies practical quality standards for security primitives by training with PUF loss. It is also demonstrated that the NNPUF response has an unassailable resistance against deep learning-based modeling attacks, which is verified by the near-50% prediction model accuracy.
Keyword:
deep learning
hardware security
memristors
physically unclonable functionss
期刊
IF:
6.1
论文数:
2.0K
被引数:
8.4K
机构
暂无机构信息
引用论文
分享收藏13C frequency shifts and the general harmonic force fields of methyl chloride, bromide and iodide
PREAI
分享收藏Memristive crypto primitive for building highly secure physical unclonable functions.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristorsNATURE ELECTRONICS
NATURE ELECTRONICSIF40.9
PREAI
分享收藏Nano Meets Security: Exploring Nanoelectronic Devices for Security ApplicationsPROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏
查看更多内容