Presentation 2021-02-05
Hardware Trojan Detection by Learning Power Side Channel Signals Considering Random Process Variation
Michiko Inoue, Riaz-Ul-Haque Mian,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Due to the globalization and complexity of the supply chain, there is a growing concern about the insertion of hardware Trojan circuits into semiconductor integrated circuits. In this paper, we propose a method to detect hardware Trojan circuits by learning side-channel signals. The method uses a power Monte Carlo simulation with random process variation annotated delay information. We propose a method to learn the dynamic power variation due to delay variation and to detectthe abnormal power value caused by hardware Trojan circuits.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) hardware Trojan circuit / random process variation / power analysis / outlier detection
Paper # DC2020-70
Date of Issue 2021-01-29 (DC)

Conference Information
Committee DC
Conference Date 2021/2/5(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Hiroshi Takahashi(Ehime Univ.)
Vice Chair Tatsuhiro Tsuchiya(Osaka Univ.)
Secretary Tatsuhiro Tsuchiya(Nihon Univ.)
Assistant

Paper Information
Registration To Technical Committee on Dependable Computing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hardware Trojan Detection by Learning Power Side Channel Signals Considering Random Process Variation
Sub Title (in English)
Keyword(1) hardware Trojan circuit
Keyword(2) random process variation
Keyword(3) power analysis
Keyword(4) outlier detection
1st Author's Name Michiko Inoue
1st Author's Affiliation Nara Institute of Science and Technology(NAIST)
2nd Author's Name Riaz-Ul-Haque Mian
2nd Author's Affiliation Nara Institute of Science and Technology(NAIST)
Date 2021-02-05
Paper # DC2020-70
Volume (vol) vol.120
Number (no) DC-358
Page pp.pp.7-11(DC),
#Pages 5
Date of Issue 2021-01-29 (DC)