Presentation 2018-03-02
Machine Learning Attack Using Selectable Challenge Set for Feed-Forward PUF
Yusuke Nozaki, Masaya Yoshikawa,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) The dreadful of machine learning attack for physical unclonable functions (PUFs) has been reported. Therefore, PUFs with a resistance against machine learning attack, such as feed-forward PUF, XOR arbiter PUF, or lightweight PUF, have been proposed. Also, to consider the security of PUFs, the tamper resistance evaluation of the feed-forward PUF against the machine learning attack is very important. This study proposes a new machine learning attack for the feed-forward PUF. The proposed method performs the machine learning attack using selectable challenge sets. Then, the proposed method estimates the response of the feed-forward PUF. Moreover, several evaluation experiments verify the validity of the proposed method.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) PUF / Feed-Forward PUF / Machine Learning Attack / Hardware Security / Tamper Resistance
Paper # VLD2017-128
Date of Issue 2018-02-21 (VLD)

Conference Information
Committee VLD / HWS
Conference Date 2018/2/28(3days)
Place (in Japanese) (See Japanese page)
Place (in English) Okinawa Seinen Kaikan
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Hiroyuki Ochi(Ritsumeikan Univ.)
Vice Chair Noriyuki Minegishi(Mitsubishi Electric)
Secretary Noriyuki Minegishi(Hiroshima City Univ.) / (NTT)
Assistant

Paper Information
Registration To Technical Committee on VLSI Design Technologies / Technical Committee on Hardware Security
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Machine Learning Attack Using Selectable Challenge Set for Feed-Forward PUF
Sub Title (in English)
Keyword(1) PUF
Keyword(2) Feed-Forward PUF
Keyword(3) Machine Learning Attack
Keyword(4) Hardware Security
Keyword(5) Tamper Resistance
1st Author's Name Yusuke Nozaki
1st Author's Affiliation Meijo University(Meijo Univ.)
2nd Author's Name Masaya Yoshikawa
2nd Author's Affiliation Meijo University(Meijo Univ.)
Date 2018-03-02
Paper # VLD2017-128
Volume (vol) vol.117
Number (no) VLD-455
Page pp.pp.237-242(VLD),
#Pages 6
Date of Issue 2018-02-21 (VLD)