Deep Learning Attack for Physical Unclonable Function

被引：0

作者：

Ikezaki, Yoshiya ^{[1
]}

Nozaki, Yusuke ^{[1
]}

Yoshikawa, Masaya ^{[2
]}

机构：

[1] Meijo Univ, Dept Informat Engn, Grad Sch, Tenpaku Ku, 1-501 Shiogamaguchi, Nagoya, Aichi, Japan

[2] Meijo Univ, Dept Informat Engn, Tenpaku Ku, 1-501 Shiogamaguchi, Nagoya, Aichi, Japan

来源：

2016 IEEE 5TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS | 2016年

关键词：

Phisycal Unclonable Functoin; security; deep learning;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The semiconductor counterfeiting has become a serious problem. Several Physical Unclonable Functions (PUFs), which utilizes the variation when manufacturing, are proposed as a countermeasure for imitation electronics. An arbiter PUF is one of the most popular PUFs. The operation of an arbiter PUF can be expressed by using a delay model. An arbiter PUF is reported to be attacked by forcing them to learn the delay model. Almost all of previous studies used SVM for the learning This study proposes a new attack method using a deep learning technique. Experiments prove the validity of the proposed method.

引用

页数：2

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